Antibody categorization based on binding characteristics

ABSTRACT

Methods for categorizing antibodies based on their epitope binding characteristics are described. Methods and systems for determining the epitope recognition properties of different antibodies are provided. Also provided are data analysis processes for clustering antibodies on the basis of their epitope recognition properties and for identifying antibodies having distinct epitope binding characteristics.

RELATED APPLICATIONS

This application is a continuation of U.S. Nonprovisional patentapplication Ser. No. 10/309,419, filed Dec. 2, 2002, now issued as U.S.Pat. No. 7,771,951, which claims priority to U.S. Provisional PatentApplication Ser. No. 60/337,245, filed Dec. 3, 2001, and U.S.Provisional Patent Application Ser. No. 60/419,387, filed Oct. 16, 2002,all of which are hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to grouping antibodies based on theepitopes they recognize and identifying antibodies having distinctbinding characteristics. In particular, the present invention relates toantibody competition assay methods for determining antibodies that bindto an epitope, and data analysis processes for dividing antigen-specificantibodies into clusters or “bins” representing distinct bindingspecificities. Specifically, the invention relates to the MultiplexedCompetitive Antibody Binning (MCAB) high-throughput antibody competitionassay and the Competitive Pattern Recognition (CPR) data analysisprocess for analyzing data generated by high-throughput assays.

BACKGROUND OF THE INVENTION

Monoclonal antibodies (mAb) show an important therapeutic utility in thetreatment of a wide variety of diseases such as infectious diseases,cardiovascular disease, inflammation, and cancer. (Storch (1998)Pediatrics 102:648-651; Coller et al. (1995) Thromb. Haemostasis74:302-308; Present et al. (1999) New Eng. J. Med. 6:1398-1405;Goldenberg (1999) Clin. Ther. 21:309-318). Cells produce antibodies inresponse to infection or immunization with a foreign substance orantigen. The potential therapeutic utility of monoclonal antibodies isin part due to their specific and high-affinity binding to a target.Antibodies bind specifically to a target antigen by recognizing aparticular site, or epitope, on the antigen. With the use of therecently developed XenoMouse® technology (Abgenix, Inc., Fremont,Calif.) together with established procedures for hybridoma cells or Bcells (Kohler and Milstein (1975) Nature 256:495-497) and isolatinglymphocytes (Babcook et al. (1996) Proc. Natl. Acad. Sci. 93:7843-7848),it is possible to generate large numbers of antigen-specific humanmonoclonal antibodies against almost any human antigen. (Green (1999)Jnl. Immunol. Methods 231:11-23, Jakobovits et al. (1993) Proc. Natl.Acad. Sci. U.S.A., 90:2551-2555, Mendez et. al. (1997) Nat. Genet.15:146-156; Green and Jakobivits, J. Exp. Med. (1998), 188:483-495).

The large numbers of antibodies generated against a particular targetantigen may vary substantially in terms of both how strongly they bindto the antigen as well as the particular epitope they bind to on thetarget antigen. Different antibodies generated against an antigenrecognize different epitopes and have varying binding affinities to eachepitope. In order to identify therapeutically useful antibodies from thelarge number of generated candidate antibodies, it is necessary toscreen large numbers of antibodies for their binding affinities andepitope recognition properties. For this reason, it would beadvantageous to have a rapid method of screening antibodies generatedagainst a particular target antigen to identify those antibodies thatare most likely to have a therapeutic effect. In addition, it would beadvantageous to provide a mechanism for categorizing the generatedantibodies according to their target epitope binding sites.

SUMMARY OF THE INVENTION

The present disclosure provides methods for categorizing antibodiesbased on their epitope binding characteristics. One aspect providesmethods and systems for determining the epitope recognition propertiesof different antibodies. Another aspect provides data analysis processesfor clustering antibodies on the basis of their epitope recognitionproperties and for identifying antibodies having distinct epitopebinding characteristics. Antibody categorization or “binning” asdisclosed and claimed herein encompasses assay methods and data analysisprocesses for determining the epitope binding characteristics of a poolof antigen-specific antibodies, clustering antibodies into “bins”representing distinct epitope binding characteristics, and identifyingantibodies having desired binding characteristics.

The method for categorizing antibodies based on binding characteristicsincludes:

a) providing a set of antibodies that bind to an antigen, labelling eachantibody in the set to form a labelled reference antibody set such thateach labelled reference antibody is distinguishable from every otherlabelled reference antibody in the labelled reference antibody set,selecting a probe antibody from the set of antibodies that bind to theantigen, contacting the probe antibody with the labelled referenceantibody set in the presence of the antigen, detecting probe antibody ina complex that includes a labelled reference antibody bound to antigen,the antigen, and probe antibody bound to antigen; and

b) providing input data representing the outcomes of at least oneantibody competition assay using a set of antibodies that bind to anantigen as in step a), normalizing the input data to generate anormalized intensity matrix, computing at least one dissimilarity matrixcomprising generating a threshold matrix from the normalized intensitymatrix and computing a dissimilarity matrix from the threshold matrix,and clustering antibodies based on dissimilarity values in cells of thedissimilarity matrix, to determine epitope binding patterns of set ofantibodies that bind to an antigen. The input data can be generated by ahigh throughput assay, preferably by the Multiplexed CompetitiveAntibody Binning (MCAB) assay. Preferably, the Competitive PatternRecognition process is used for data analysis.

For the antibody competition assay method, the probe antibody may belabelled, and detected in a complex that includes a labelled referenceantibody bound to antigen, antigen, and labelled probe antibody bound toantigen, which allows determination whether the labelled probe antibodycompetes with any reference antibody in the labelled reference antibodyset, because competition indicates that the probe antibody binds to thesame epitope as another antibody in the set of antibodies that bind toan antigen. The probe antibody can be labelled, for example, with anenzymatic label, or a colorimetric label, or a fluorescent label, or aradioactive label.

In a preferred embodiment of the antibody competition assay method, adetection antibody is used to detect bound probe antibody, where thedetection antibody binds only to probe antibody and not to referenceantibody. The detection antibody detects bound probe antibody in complexthat includes a labelled reference antibody bound to antigen, antigen,and labelled probe antibody bound to antigen. A labelled detectionantibody is used to detect bound probe antibody, where the detectionantibody can be labelled, for example, with an enzymatic label, or acolorimetric label, or a fluorescent label, or a radioactive label.Alternately, the detection antibody is detected using a detection meanssuch as an antibody-binding protein.

In particular, the antibody competition assay method for determiningantibodies that bind to an epitope on an antigen includes providing aset of antibodies that bind to an antigen, labelling each antibody inthe set with a uniquely colored bead to form a labelled referenceantibody set such that each labelled reference antibody isdistinguishable from every other labelled reference antibody in thelabelled reference antibody set, selecting a probe antibody from the setof antibodies that bind to the antigen, contacting the probe antibodywith the labelled reference antibody set in the presence of the antigen,detecting bound probe antibody in a complex that includes a labelledreference antibody bound to antigen, antigen, and probe antibody boundto antigen, and determining whether the probe antibody competes with anyreference antibody in the labelled reference antibody set, wherecompetition indicates that the probe antibody binds to the same epitopeas another antibody in the set of antibodies that bind to an antigen.Each uniquely colored bead may have a distinct emission spectrum. Theprobe antibody can be labelled, for example, with an enzymatic label, ora colorimetric label, or a fluorescent label, or a radioactive label.Alternately, a detection antibody is used to detect bound probeantibody, where the detection antibody may be labelled. The labelleddetection antibody can be labelled, for example, with an enzymaticlabel, or a colorimetric label, or a fluorescent label, or a radioactivelabel.

Another aspect of the present invention provides a method forcharacterizing antibodies based on binding characteristics by providinginput data representing the outcomes of at least one antibodycompetition assay using a set of antibodies that bind to an antigen,normalizing the input data to generate a normalized intensity matrix,computing at least one dissimilarity matrix comprising generating athreshold matrix from the normalized intensity matrix and computing adissimilarity matrix from the threshold matrix, and clusteringantibodies based on dissimilarity values in cells of the dissimilaritymatrix, to determine epitope binding patterns of set of antibodies thatbind to an antigen.

The input data can be signal intensity values representing the outcomesof an antibody competition assay using a set of antibodies that bind toan antigen. The input data representing the outcomes of an antibodycompetition assay can be stored in matrix form. The input data stored inmatrix form can be in a two-dimensional matrix or a multidimensionalmatrix, and may be stored in a plurality of matrices. The input datastored in matrix form can be signal intensity values representing theoutcomes of an antibody competition assay. The antibody competitionassay can be the Multiple Competitive Antibody Binning (MCAB) assay. Theinput data stored in matrix form can be at least one matrix wherein eachcell of the matrix comprises the signal intensity value of an individualantibody competition assay.

Normalizing the input data to generate a normalized intensity matrix caninclude generating a background-normalized intensity matrix bysubtracting a first matrix with signal intensity values from a firstantibody competition assay in which antigen was not added (negativecontrol) from a second matrix with signal intensity values from a secondantibody competition assay in which antigen was added. A minimumthreshold value for blocking buffer values is set, and any blockingbuffer values below the threshold value are adjusted to the thresholdvalue prior to said generating the normalized intensity matrix.

If desired, the normalizing step includes generating anintensity-normalized matrix by dividing each value in a column of thebackground-normalized intensity matrix by the blocking buffer intensityvalue for the column. The normalizing step can further includenormalizing relative to the baseline signal for probe antibodies bydividing each column of the intensity-normalized matrix by itscorresponding diagonal value to generate a final intensity-normalizedmatrix. Prior to dividing each column by its corresponding diagonalvalue, each diagonal value is compared with a user-defined thresholdvalue and any said diagonal value below the user-defined threshold valueis adjusted to the threshold value.

If desired, the normalizing step includes generating anintensity-normalized matrix by dividing each value in a row of thebackground-normalized intensity matrix by the blocking buffer intensityvalue for the row. The normalizing step can further include normalizingrelative to the baseline signal for probe antibodies by dividing eachrow of the intensity-normalized matrix by its corresponding diagonalvalue to generate a final intensity-normalized matrix. Prior to dividingeach row by its corresponding diagonal value, each diagonal value iscompared with a user-defined threshold value and any said diagonal valuebelow the user-defined threshold value is adjusted to the thresholdvalue.

Generating the threshold matrix involves setting the normalized valuedin each cell of the normalized intensity matrix to a value of one (1) orzero (0), wherein normalized values less than or equal to a thresholdvalue are set to a value of zero (0) and normalized values greater to athreshold value are set to a value of one (1).

At least one dissimilarity matrix is computed from the threshold matrixof ones and zeroes by determining the number of positions in which eachpair of rows differs. A plurality of dissimilarity matrices can becomputed using a plurality of threshold values. The average of aplurality of dissimilarity matrices can computed and used as input tothe clustering step.

Clustering antibodies based on said dissimilarity values in cells ofsaid dissimilarity matrix can include hierarchical clustering.Hierarchical clustering includes generating a hierarchy of nestedsubsets of antibodies within a set of antibodies that bind to an antigenby determining the pair of antibodies in the set having the lowestdissimilarity value, then determining the pair of antibodies having thenext lowest dissimilarity value, and iteratively repeating thisdetermining each pair of antibodies having the next lowest dissimilarityvalue until one pair of antibodies remains, such that a hierarchy ofnested subsets is generated that indicates the similarity of competitionpatterns within the set of antibodies. Clusters are determined based oncompetition patterns.

Alternately, the data analysis process involves subtracting the datamatrix for the experiment carried out with antigen from the data matrixfor the experiment without antigen. The value in each diagonal cell isthen used as a background value for determining the binding affinity ofthe antibody in the corresponding column. Cells in the subtracted matrixhaving values significantly higher than the corresponding diagonal valueare highlighted or otherwise noted.

Data from the clustering step can be captured, including by automatedmeans. In particular, data from the clustering step can be captured in aformat compatible with data input device or computer. The clusteringstep can generate a display, which can be in a format compatible withdata input device or computer. The display generated by the clusteringstep can be a dissimilarity matrix. Clusters can be determined by visualinspection of the dissimilarity value in each cell of the dissimilaritymatrix. The dissimilarity matrix can include cells having a visualindicator of the cluster to which the antibody pair represented by saidcell belongs, where the visual indicator may be a color, or shading, orpatterning. Alternately, the display can be a dendrogram defined bydissimilarity values computed for a set of antibodies. Such a dendrogramhas branches representing antibodies in the set of antibodies, whereinthe arrangement of branches represents relationships between antibodieswithin the set of antibodies, and the arrangement further representsclusters of antibodies within the set of antibodies. In such adendrogram, the length of any branch represents the degree of similaritybetween the binding pattern of antibodies or cluster of antibodiesrepresented by said branch.

Input data representing the outcomes of a plurality of antibodycompetition assays can be analyzed, wherein each assay represents anindividual experiment using a set of antibodies that bind to an antigen,further wherein each experiment includes at least one antibody that isalso tested in at least one other experiment. When a plurality ofexperiments is analyzed, an individual normalized intensity matrix canbe generated for each individual experiment and a single normalizedintensity matrix can be generated by computing the average intensityvalue of each antibody pair represented in each individual normalizedintensity matrix. A single dissimilarity matrix representing eachantibody pair tested in the plurality of antibody competition assays canbe generated from the single normalized intensity matrix.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Schematic illustration of one embodiment of an epitope binningassay using labelled bead technology in a single well of a microliterplate. Each reference antibody is coupled to a bead with distinctemission spectrum, forming a uniquely labelled reference antibody. Theentire set of uniquely labelled reference antibodies is placed in thewell of a multiwell microtiter plate and incubated with antigen. A probeantibody is added and the interaction of probe antibody with eachuniquely labelled reference antibody is determined.

FIG. 2. Correlation between blocking buffer intensity values and averageintensity. FIG. 2A. Correlation between blocking buffer intensity andaverage intensity within rows. Blocking buffer intensity value for eachrow (y-axis) plotted against the average intensity value of the row withblocking buffer value omitted (x-axis). Fitting a line to the data showsa strong linear correlation between the blocking buffer values and theaverage intensity values of the rest of the row. FIG. 2B. Correlationbetween blocking buffer intensity and average intensity within columns.Blocking buffer intensity value for each column (y-axis) plotted againstthe average intensity value of the column with blocking buffer valueomitted (x-axis). Fitting a line to the data shows a relatively weaklinear correlation between the blocking buffer values and the averageintensity values of the rest of the column. FIG. 2C. Scatter plot ofintensity values for the matrix with antigen and background-normalizedmatrix. This plot shows a tight linear correlation (slope about 1.0) forhigh subtracted signal values, indicating that the background signal isminimal relative to the signal in the presence of antigen. The pointsare shaded according to the value of the fraction, calculated as thesubtracted signal divided by the signal for the experiment with antigenpresent. Smaller fraction values (closer to zero) correspond to highbackground contribution and have light shading. Larger fraction values(closer to 1) correspond to lower background contribution and havedarker shading.

FIG. 3. Comparison of epitope binning results with FACS results. Resultsfrom antibody experiments using the ANTIGEN39 antibody are shown,comparing results using the epitope binning method described herein withresults using flow cytometry (fluorescence-activated cell sorter, FACS).Antibodies are assigned to bins 1-15, as indicated by rows 1-15 in thefar left column using the epitope binning assay. Hatching in cellsindicates antibodies that are FACS positive for cells expressingANTIGEN39 (cell line 786-0), and no hatching indicates antibodies thatare negative for cells that do not express ANTIGEN39 (cell line M14).

FIG. 4. Dissimilarity vs. background value: effect of choice ofthreshold cutoff value. The figure shows the amount of dissimilaritybetween antibodies 2.1 and 2.25 calculated at various threshold values.The amount of dissimilarity represents the value for the dissimilaritymatrix for the entry corresponding to the two antibodies, Ab 2.1 and Ab2.25 for a series of dissimilarity matrices computed using differentthreshold values. Here, the x-axis is the threshold value, and they-axis is the dissimilarity value calculated using that threshold cutoffvalue.

FIG. 5. Dendrogram for the ANTIGEN14 antibodies. The length of branchesconnecting two antibodies is proportional to the degree of similaritybetween the two antibodies. This figure shows that there are two verydistinct epitopes recognized by these antibodies. One epitope isrecognized by antibodies 2.73, 2.4, 2.16, 2.15, 2.69, 2.19, 2.45, 2.1,and 2.25. A different epitope is recognized by antibodies 2.13, 2.78,2.24, 2.7, 2.76, 2.61, 2.12, 2.55, 2.31, 2.56, and 2.39. Antibody 2.42does not have a pattern that is very similar to any other antibody, buthas some noticeable similarity to the second cluster, although it mayrecognize yet a third epitope which partially overlaps with the secondepitope.

FIG. 6. Dendrograms for ANTIGEN39 antibodies. FIG. 6A. Dendrogram forthe ANTIGEN39 antibodies for five input experimental data sets. Thenumber o unique clusters of antibodies suggests that are severaldifferent epitopes, some of which may overlap. For example, the clustercontaining antibodies 1.17, 1.55, 1.16, 1.11 and 1.12 and the clustercontaining 1.21, 2.12, 2.38, 2.35, and 2.1 appear to be fairly closelyrelated, with each antibody pair with the exception of 2.35 and 1.11being no more than 25% different. This high degree of similarity acrossthe two clusters suggests that the two different epitopes themselveshave a high degree of similarity. FIG. 6B. Dendrogram for the ANTIGEN39antibodies for Experiment 1. Antibodies 1.12, 1.63, 1.17, 1.55, and 2.12consistently cluster together in this experiment as well as in otherexperiments, as do antibodies 1.46, 1.31, 2.17, and 1.29. FIG. 6C.Dendrogram for the ANTIGEN39 antibodies for Experiment 2. Antibodies1.57 and 1.61 consistently cluster together in this experiment as wellas in other experiments. FIG. 6D. Dendrogram for the ANTIGEN39antibodies for Experiment 3. Antibodies 1.55, 1.12, 1.17, 2.12, 1.11 and1.21 consistently cluster together in this experiment as well as inother experiments. FIG. 6E. Dendrogram for the ANTIGEN39 antibodies forExperiment 4. Antibodies 1.17, 1.16, 1.55, 1.11 and 1.12 consistentlycluster together in this experiment as well as in other experiments, asdo antibodies 1.31, 1.46, 1.65, and 1.29, as well as antibodies 1.57 and1.61. FIG. 6F. Dendrogram for the ANTIGEN39 antibodies for Experiment 5.Antibodies 1.21, 1.12, 2.12, 2.38, 2.35, and 2.1 consistently clustertogether in this experiment as well as in other experiments.

FIG. 7. Dendrograms for clustering IL-8 monoclonal antibodies. FIG. 7A.Dendrograms for a clustering of seven IL-8 monoclonal antibodies. Thedendrogram on the left is generated by clustering columns, and thedendrogram on the right by clustering rows of a background-normalizedsignal intensity matrix. Both dendrograms indicate that there are twoepitopes, using a dissimilarity cutoff of 0.25: one epitope isrecognized by monoclonal antibodies HR26, a215, a203, a393, and a452; asecond epitope is recognized by monoclonal antibodies K221 and a33. FIG.7B. Dendrograms for IL-8 monoclonal antibodies from a combinedclustering analysis merging five different experimental data sets. Thedendrogram on the left was generated by clustering columns, whereas thedendrogram on the right was generated by clustering rows of thebackground-normalized signal intensity matrix. Both dendrograms indicatethat there are two epitopes, using a dissimilarity cut-off of 0.25: oneepitope is recognized by monoclonal antibodies a809, a928, HR26, a215,and D111; a second epitope is recognized by monoclonal antibodies a837,K221, a33, a142, a358, and a203, a393, and a452. FIG. 7C. Dendrogramsfor a clustering of nine IL-8 monoclonal antibodies. The dendrogram onthe left was generated by clustering columns, and the dendrograms on theright by clustering rows of the background-normalized signal intensitymatrix. Both dendrograms indicate that there are two epitopes, using adissimilarity cut-off of 0.25: one epitope is recognized by monoclonalantibodies HR26 and a215; a second epitope is recognized by monoclonalantibodies K221, a33, a142, a203, a358, a393, and a452.

FIG. 8. Intensity matrices generated in the embodiment disclosed inExample 2 using a set of antibodies against ANTIGEN14. FIGS. 8A and 8Bare tables showing the intensity matrix for experiment conducted withantigen. FIGS. 8C and 8D are tables showing the intensity matrix for thesame experiment conducted without antigen (control). These matrices areused as input data matrices for subsequence steps in data analysis.

FIG. 9. Difference matrix for antibodies against the ANTIGEN14 target.Difference matrix is generated by subtracting the matrix correspondingto values obtained from experiment without antigen (see FIG. 8B) fromthe matrix corresponding to values obtained from the experiment withantigen (see FIG. 8A) disclosed in Example 2.

FIG. 10. Adjusted difference matrix with minimum threshold value. Forthe intensity values of Example 2, the minimum reliable signal intensityvalue is set to 200 intensity units and values below the minimumthreshold are set to the threshold of 200.

FIG. 11. Row normalized matrix. Each row in the adjusted differencematrix of FIG. 10 is adjusted by dividing it by the last intensity valuein the row, which corresponds to the intensity value for beads to whichblocking buffer is added in place of primary antibody. This adjusts forwell-to-well intensity.

FIG. 12. Diagonal normalized matrix. All columns except the onecorresponding to Antibody 2.42 were column-normalized. Dividing eachcolumn by its corresponding diagonal is carried out to measure eachintensity relative to an intensity that is known to reflectcompetition—i.e., competition against self.

FIG. 13. Antibody pattern recognition matrix. For data from theembodiment disclosed in Example 2, intensity values below theuser-defined threshold were set to zero. The user-defined threshold wasset to two (2) times the diagonal intensity values. Remaining valueswere set to one.

FIG. 14. Dissimilarity matrix. For data from the embodiment disclosed inExample 2, a dissimilarity matrix is generated from the matrix of zeroesand ones shown in FIG. 13, by setting the entry in row i and column j tothe fraction of the positions at which two rows, i and j, differ. FIG.14 shows the number of positions, out of 22 total, at which the patternsfor any two antibodies differed for set of antibodies generated againstthe ANTIGEN14 target.

FIG. 15. Average dissimilarity matrix. After separate dissimilaritymatrices were generated from each of several threshold values rangingfrom 1.5 to 2.5 times the values of the diagonals, the average of thesedissimilarity matrices was computed (FIG. 15) and used as input to theclustering process.

FIG. 16. Permuted average dissimilarity matrix. For data from theembodiment disclosed in Example 2, clusters can be visualized inmatrices. In FIG. 16, the rows and columns of the dissimilarity matrixwere rearranged according to the order of the “leaves” or clades on thedendrogram shown in FIG. 5, and individual cells were visually codedaccording to the degree of dissimilarity.

FIG. 17. Permuted normalized intensity matrix. For data from theembodiment disclosed in Example 2, rows and columns of the normalizedintensity matrix were rearranged according to the order of the leaves onthe dendrogram shown in FIG. 5, and individual cells were visually codedaccording to their normalized intensity values.

FIG. 18. Permuted average dissimilarity matrix for five ANTIGEN39 inputdata sets. Data from five experiments that were conducted usingantibodies against the ANTIGEN39 target (see Example 3) produced fiveinput data sets. Dissimilarity matrices were generated for each inputdata set, and an average dissimilarity matrix was generated, and rowsand columns were arranged (permuted) according to arrangement of thecorresponding dendrogram(s) shown in FIG. 6.

FIG. 19. Permuted normalized intensity matrix for five ANTIGEN39 inputdata sets. Data from five experiments that were conducted usingantibodies against the ANTIGEN39 target (see Example 3) produced fiveinput data sets. A normalized intensity matrix was generated for thefive input data sets and rows and columns were arranged (permuted)according to arrangement of the corresponding dendrogram(s) shown inFIG. 6.

FIG. 20. Permuted average dissimilarity matrix for Experiment 1 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 1 (Example 3) were analyzed. Seedendrogram shown in FIG. 6B.

FIG. 21. Permuted normalized intensity matrix for Experiment 1 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 1 (Example 3) were analyzed. Seedendrogram shown in FIG. 6B.

FIG. 22. Permuted average dissimilarity matrix for Experiment 2 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 2 (Example 3) were analyzed. Seedendrogram shown in FIG. 6C.

FIG. 23. Permuted normalized intensity matrix for Experiment 2 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 2 (Example 3) were analyzed. Seedendrogram shown in FIG. 6C.

FIG. 24. Permuted average dissimilarity matrix for Experiment 3 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 3 (Example 3) were analyzed. Seedendrogram shown in FIG. 6D

FIG. 25. Permuted normalized intensity matrix for Experiment 3 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 3 (Example 3) were analyzed. Seedendrogram shown in FIG. 6D.

FIG. 26. Permuted average dissimilarity matrix for Experiment 4 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 4 (Example 3) were analyzed. Seedendrogram shown in FIG. 6E.

FIG. 27. Permuted normalized intensity matrix for Experiment 4 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 4 (Example 3) were analyzed. Seedendrogram shown in FIG. 6E.

FIG. 28. Permuted average dissimilarity matrix for Experiment 5 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 5 (Example 3) were analyzed. Seedendrogram shown in FIG. 6F.

FIG. 29. Permuted normalized intensity matrix for Experiment 5 using aset of antibodies against the ANTIGEN39 target. Data from the set ofantibodies analyzed in Experiment 5 (Example 3) were analyzed. Seedendrogram shown in FIG. 6F.

FIG. 30. Clusters identified in Experiments 1-5 using sets of antibodiesagainst the ANTIGEN39 target. FIG. 30 summarizes the clusters identifiedfor each of the five individual data sets and for the combined data setfor all of the antibodies generated in all five experiments disclosed inExample 3.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

With increased fusion efficiency producing larger numbers of antigenspecific antibodies from each hybridoma-cell fusion experiment, ascreening method of managing and prioritizing large numbers ofantibodies becomes ever more important. When a set of monoclonalantibodies has been generated against a target antigen, differentantibodies in the set will recognize different epitopes, and will alsohave variable binding affinities. Thus, to effectively screen largenumbers of antibodies it is important to determine which epitope eachantibody binds, and to determine binding affinity for each antibody.

Epitope binning, as described herein, is the process of groupingantibodies based on the epitopes they recognize. More particularly,epitope binning comprises methods and systems for discriminating theepitope recognition properties of different antibodies, combined withcomputational processes for clustering antibodies based on their epitoperecognition properties and identifying antibodies having distinctbinding specificities. Accordingly, embodiments include assays fordetermining the epitope binding properties of antibodies, and processesfor analyzing data generated from such assays.

In general, the invention provides an assay to determine whether a testmoiety (such as an antibody) binds to a test object (such as an antigen)in competition with other test moieties (such as other antibodies). Acapture moiety is used to capture the test object and/or the test moietyin an addressable manner and a detection moiety is utilized toaddressably detect binding between other test moieties and the testobject. When a test moiety binds to the same or similar location on thetest subject as the test moiety being assayed, no binding is detected,whereas when a test moiety binds to a different location on the testsubject as the test moiety being assayed, binding is detected. In eachcase, the binding or lack thereof is addressable, so the relativeinteractions between test moieties with the test object can be readilyascertained and categorized.

One embodiment of the invention is a competition-based method ofcategorizing a set of antibodies that have been generated against anantigen. This method relies upon carrying out a series of assays whereineach antibody from the set is tested for competitive binding against allother antibodies from the set. Thus, each antibody will be used in twodifferent modes: in at least one assay, each antibody will be used in“detect” mode as the “probe antibody” that is tested against all theother antibodies in the set; in other assays, the antibody will be usedin “capture” mode as a “reference antibody” within the set of referenceantibodies being assayed. Within the set of reference antibodies, eachreference antibody will be uniquely labelled in a way that permitsdetection and identification each reference antibody within a mixture ofreference antibodies. The method relies on forming “sandwiches” orcomplexes involving reference antibodies, antigen, and probe antibody,and detecting the formation or lack of formation of these complexes.Because each reference antibody in the set is uniquely labelled, it ispossible to addressably determine whether a complex has formed for eachreference antibody present in the set of reference antibodies beingassayed.

Antibody Assay Overview

The method begins by selecting an antibody from the set of antibodiesagainst an antigen, where the selected antibody will serve as the “probeantibody” that is to be tested for competitive binding against all otherantibodies of the set. A mixture containing all the antibodies willserve as a set of “reference antibodies” for the assay, where eachreference antibody in the mixture is uniquely labelled. In an assay, theprobe antibody is contacted with the set of reference antibodies, in thepresence of the target antigen. Accordingly, a complex will form betweenthe probe antibody and any other antibody in the set that does notcompete for the same epitope on the target antigen. A complex will notform between the probe antibody and any other antibody in the set thatcompetes for the same epitope on the target antigen. Formation ofcomplexes is detected using a labelled detection antibody that binds theprobe antibody. Because each reference antibody in the mixture isuniquely labelled, it is possible to determine for each referenceantibody whether that reference antibody does or does not form a complexwith the probe antibody. Thus, it can be determined which antibodies inthe mixture compete with the probe antibody and bind to the same epitopeas the probe antibody.

Each antibody is used as the probe antibody in at least one assay. Byrepeating this method of testing each individual antibody in the setagainst the entire set of antibodies, the competitive binding affinitiescan be generated for the entire set of antibodies against an antigen.From such a affinity measurements, one can determine which antibodies inthe set have similar binding characteristics to other antibodies in theset, thereby allowing the grouping or “binning” of each antibody on thebasis of its epitope binding profile. A table of competitive bindingaffinity measurements is a suitable method for displaying assay results.A preferred embodiment of this method is the Multiplexed CompetitiveAntibody Binning (MCAB) assay for high-throughput screening ofantibodies.

Because this embodiment relies on testing antibody competition, whereina single antibody is tested against the entire set of antibodiesgenerated against an antigen, one challenge to implementing this methodrelates to the mechanism used to uniquely identify and quantitativelymeasure complexes formed between the single antibody and any one of theother antibodies in the set. It is this quantitative measurement thatprovides an estimate of whether two antibodies are competing for thesame epitope on the antigen.

As described below, embodiments of the invention relate to uniquelylabelling each reference antibody in the set prior to creating a mixtureof all antibodies. This unique label, as discussed below, is not limitedto any particular mechanism. Rather, it is contemplated that any methodthat provides a way to identify each reference antibody within themixture, allowing one to distinguish each reference antibody in the setfrom every other reference antibody in the set, would be suitable. Forexample, each reference antibody can be labelled colorimetrically sothat the particular color of each antibody in the set is determinable.Alternatively, each reference antibody in the set might be labelledradioactively using differing radioactive isotopes. The referenceantibody may be labelled by coupling, linking, or attaching the antibodyto a labelled object such as a bead or other surface.

Once each reference antibody in the set has been uniquely labelled, amixture is formed containing all the reference antibodies. Antigen isadded to the mixture, and the probe antibody is added to the mixture. Adetection label is necessary in order to detect complexes containingbound probe antibody. A detection label may be a labelled detectionantibody or it may be another label that binds to the probe antibody.For example, when a set of human monoclonal antibodies is being tested,a mouse anti-human monoclonal antibody is suitable for use as adetection antibody. The detection label is chosen to be distinct fromall other labels in the mixture that are used to label referenceantibodies. For example, a labelled detection antibody might be labelledwith a unique color, or radioactively labelled, or labelled by aparticular fluorescent marker such as phycoerythrin (PE).

The design of an experiment must include selecting conditions such thatthe detection antibody will only bind to the probe antibody, and willnot bind to the reference antibodies. In embodiments in which referenceantibodies are coupled to beads or other materials through antibodies,the antibody that couples the reference antibody to the bead (the“capture antibody”) will be the same antibody as the detection antibody.In accordance with this embodiment of the invention, the detectionantibody is specifically chosen or modified so that the detectionantibody binds only to the probe antibody and does not bind to thereference antibody. By using the same antibody for both detection andcapture, each will block one the other from binding to their respectivetargets. Accordingly, when the capture antibody is bound to thereference antibody, it will block the detection antibody from binding tothe same epitope on the reference antibody and producing a falsepositive result. Antibodies suitable for use as detection antibodiesinclude mouse anti-human IgG2, IgG3, and IgG4 antibodies available fromCalbiochem, (Catalog No. 411427, mouse anti-human IgKappa available fromSouthern Biotechnology Associates, Inc. (Catalog Nos. 9220-01 and9220-08, and mouse anti-hIgG from PharMingen (Catalog Nos. 555784 and555785).

Once the labelled detection antibody has been added to the mixture, theentire mixture can then be analyzed to detect complexes between labelleddetection antibody, bound probe antibody, the antigen, and uniquelylabelled reference antibody. The detection method must permit detectionof complexes (or lack thereof) for each uniquely labelled referenceantibody in the mixture.

Detecting whether a complex formed between a probe antibody and eachreference antibody in the set indicates, for each reference antibody,whether that reference antibody competes with the probe antibody forbinding to the same (or nearby) epitope. Because the mixture ofreference antibodies will include the antibody being used as the probeantibody, it is expected that this provides a negative control.Detecting complex formation allows measurement of competitive affinitiesof the antibodies in the set being tested. This measurement ofcompetitive affinities is then used to categorize each antibody in theset based on how strongly or weakly they bind to the same epitopes onthe target antigen. This provides a rapid method for grouping antibodiesin a set based on their binding characteristics.

In one embodiment, large numbers of antibodies can be simultaneouslyscreened for their epitope recognition properties in a single experimentin accordance with embodiments of the present invention, as describedbelow. Generally, the term “experiment” is used nonexclusively herein toindicate a collection of individual antibody assays and suitablecontrols. The term “assay” is used nonexclusively herein to refer toindividual assays, for example reactions carried out in a single well ofa microtiter plate using a single probe antibody, or may be used torefer to a collection of assays or to refer to a method of measuringantibody binding and competition as described herein.

In one embodiment, large numbers of antibodies are simultaneouslyscreened for their epitope recognition properties using a sandwich assayinvolving a set of reference antibodies in which each reference antibodyin the set is bound to a uniquely labelled “capture” antibody. Thecapture antibody can be, for example, a colorimetrically labelledantibody that has strong affinity for the antibodies in the set. As oneexample, the capture antibody can be a labelled mouse, goat, or bovineanti-human IgG or anti-human IgKappa antibody. Although embodimentsdescribed herein use a mouse monoclonal anti-human IgG antibody, othersimilar capture antibodies that will bind to the antibodies beingstudied are within the scope of the invention. Thus, one of skill in theart can select an appropriate capture antibody based on the origin ofthe set of antibodies being tested.

One embodiment of the present invention therefore provides a method ofcategorizing, for example, which epitopes on a target antigen are boundby fifty (50) different antibodies generated against that targetantigen. Once the 50 antibodies have been determined to have someaffinity for a target antigen, the methods described below are used todetermine which antibodies in the group of 50 bind to the same epitope.These methods are performed by using each one of the 50 antibodies as aprobe antibody to cross-compete against a mixture of all 50 antibodies(the reference antibodies), wherein the 50 uniquely labelled referenceantibodies in the mixture are each labelled by a capture antibody. Thoseantibodies that recognize the same epitope will compete with oneanother, while antibodies that do not compete are assumed to not bind tothe same epitope. By uniquely labelling a large number of antibodies ina single reaction, as described below, these methods allow for apre-selected antibody to be competed against 10, 25, 50, 100, 200, 300,or more antibodies at one time. For this reason, the choice of testing50 antibodies in an experiment is arbitrary, and should not be viewed aslimiting on the invention.

Preferably, the Multiplex Competitive Antibody Binning (MCAB) assay isused. More preferably, the MCAB assay is practiced utilizing the LUMINEXSystem (Luminex Corp., Austin Tex.), wherein up to 100 antibodies can bebinned simultaneously using the method illustrated in FIG. 1. The MCABassay is based on the competitive binding of two antibodies to a singleantigen molecule. The entire set of antibodies to be characterized isused twice in the MCAB assay, in “capture” and “detect” modes in theMCAB sandwich assay.

In one embodiment, each capture antibody is uniquely labelled. Once acapture antibody has been uniquely labelled, it is exposed to one of theset of antibodies being tested, forming a reference antibody that isuniquely labelled. This is repeated for the remaining antibodies in theset so that each antibody becomes labelled with a different coloredcapture antibody. For example, when 50 antibodies are being tested, alabelled reference antibody mixture is created by mixing all 50 uniquelylabelled reference antibodies into a single reaction well. For thisreason, it is useful for each label to have a distinct property thatallows it to be distinguished or detected when mixed with other labels.In one preferred embodiment, each capture antibody is labelled with adistinct pattern of fluorochromes so they can be colorimetricallydistinguished from one another.

Once the test antibody mixture is created, it is placed into multiplewells of, for example, a microtiter plate. In this example, the sameantibody mixture would be placed in each of 50 microtiter wells and themixture in each well would then be incubated with the target antigen asa first step in the competition assay. After incubation with the targetantigen, a single probe antibody selected from the original set of 50antibodies is added to each well. In this example, only one probeantibody is added to each reference antibody mixture. If any labelledreference antibody in the well binds to the target antigen at the sameepitope as the probe antibody, they will compete with one another forthe epitope binding site.

It is understood by one of skill in the art that embodiments of theinvention are not limited to only adding a single probe antibody to eachwell. Other methods wherein multiple probe antibodies, each onedistinguishably labelled from one another, are added to the mixture arecontemplated.

In order to determine whether the probe antibody has bound to any of the50 labelled reference antibodies in the well, a labelled detectionantibody is added to each of the 50 reactions. In one embodiment, thelabelled detection antibody is a differentially labelled version of thesame antibody used as the capture antibody. Thus, for example, thedetection antibody can be a mouse anti-human IgG antibody or aanti-human IgKappa antibody. The detection antibody will bind to, andlabel, the probe antibody that was placed in the well.

The label on the detection antibody permits detection and measurement ofthe amount of probe antibody bound to a complex formed by a referenceantibody, the antigen, and the probe antibody. This complex serves as ameasurement of the competition between the probe antibody and thereference antibody. The detection antibody may be labelled with anysuitable label which facilitates detection of the secondary antibody.For example, a detection antibody may be labelled with biotin, whichfacilitates fluorescent detection of the probe antibody whenstreptavidin-phycoerythrin (PE) is added. The detection antibody may belabelled with any label that uniquely determines its presence as part ofa complex, such as biotin, digoxygenin, lectin, radioisotopes, enzymes,or other labels. If desired, the label may also facilitate isolation ofbeads or other surfaces with antibody-antigen complexes attached.

The amount of labelled detection antibody bound to each uniquelylabelled reference antibody indicates the amount of bound probeantibody, and the labelled detection antibody is bound to the probeantibody bound to antigen bound to labelled reference antibody.Measuring the amount of labelled detection antibody bound to each one ofthe 50 labelled reference antibodies indicates the amount of bound probeantibody can be obtained, where the amount of bound probe antibody is anindicator of the similarity or dissimilarity of the epitope recognitionproperties of the two antibodies (probe and reference). If a measurableamount of the labelled detection antibody is detected on the labelledreference antibody-antigen complex, that is understood to indicate thatthe probe antibody and the reference antibody do not bind to the sameepitope on the antigen. Conversely, if little or no measurable detectionantibody is detected on the labelled reference antibody-antigen complex,then it is understood to indicate that the probe antibody for thatreaction bound to very similar or identical epitopes on the antigen. Ifa small amount of detection antibody is detected on the referenceantibody-antigen complex, that is understood to indicate that thereference and probe antibodies may have similar but not identicalepitope recognition properties, e.g., the binding of the referenceantibody to its epitope interferes with but does not completely inhibitbinding of the probe antibody to its epitope.

Another aspect of the present invention provides a method for detectingboth the reference antibody and the amount of probe antibody bound to anantigen. If antibody complexes containing different reference antibodieshave been mixed, then the unique property provided by the unique labelson the capture antibody can be used to identify the reference antibodycoupled to that bead. Preferably, that distinct property is a uniqueemission spectrum.

The amount of probe antibody bound to any reference antibody can bedetermined by measuring the amount of detection label bound to thecomplex. The detection label may be a labelled detection antibody boundto probe antibody bound to the complex, or it may be a label attached tothe probe antibody. Thus, the epitope recognition properties of both areference antibody and a probe antibody can be measured by using acomparative measure of the competition between the two antibodies for anepitope.

Conditions for optimizing procedures can be determined by empiricalmethods and knowledge of one of skill in the art. Incubation time,temperature, buffers, reagents, and other factors can be varied until asufficiently strong or clear signal is obtained. For example, theoptimal concentration of various antibodies can be empiricallydetermined by one of skill in the art, by testing antibodies andantigens at different concentrations and looking for the concentrationthat produces the strongest signal or other desired result. In oneembodiment, the optimal concentration of primary and secondaryantibodies—that is, antibodies to be binned—is determined by a doubletitration of two antibodies raised against different epitopes of thesame antigen, in the presence of a negative control antibody that doesnot recognize the antigen.

Assays Using Colored Beads

In a preferred embodiment, large numbers of antibodies aresimultaneously screened for their epitope recognition properties in asingle assay using color-coded microspheres or beads to identifymultiple reactions in a single tube or well, preferably using a systemavailable from Luminex Corporation (Luminex Corp, Austin Tex.), and mostpreferably using the Luminex 100 system. Preferably, the MCAB assay iscarried out using Luminex technology. In another preferred embodiment,up to 100 different antibodies to be tested are bound to Luminex beadswith 100 distinct colors. This system provides 100 different sets ofpolystyrene beads with varying amounts of fluorochromes embedded. Thisgives each set of beads a distinct fluorescent emission spectrum andhence a distinct color code.

To characterize the binding properties of antibodies using the Luminex100 system, beads are coated with a capture antibody which is covalentlyattached to each bead; preferably a mouse anti-human IgG or anti-humanIgKappa monoclonal antibody is used. Each set of beads is then incubatedin a well containing a reference antibody to be characterized (e.g.,containing hybridoma supernatant) such that a complex if formed betweenthe bead, the capture antibody, and the reference antibody (henceforth,a “reference antibody-bead” complex) which has a distinct fluorescenceemission spectrum and hence, a color code, that provides a unique labelfor that reference antibody.

In this preferred embodiment, each reference antibody-bead complex fromeach reaction with each reference antibody is mixed with other referenceantibody-bead complexes to form a mixture containing all the referenceantibodies being tested, where each reference antibody is uniquelylabelled by being couple to a bead. The mixture is aliquotted into asmany wells of a 96-well plate as is necessary for the experiment.Generally, the number of wells will be determined by the number of probeantibodies being tested, along with various controls. Each of thesewells containing an aliquot of the mixture of reference antibody-beadcomplexes is incubated first with antigen and then probe antibody (oneof the antibodies to be characterized), and then detection antibody (alabelled version of the original capture antibody), where the detectionantibody is used for detection of bound probe antibody. In a preferredembodiment, the detection antibody is a biotinylated mouse anti-humanIgG monoclonal antibody. This process is illustrated in FIG. 1.

In the illustrative embodiment presented in FIG. 1, each referenceantibody is coupled to a bead with distinct emission spectrum, where thereference antibody is coupled through a mouse anti-human monoclonalcapture antibody, forming a uniquely labelled reference antibody. Theentire set of uniquely labelled reference antibodies is placed in thewell of a multiwell microtiter plate. The set of reference antibodiesare incubated with antigen, and then a probe antibody is added to thewell. A probe antibody will only bind to antigen that is bound to areference antibody that recognizes a different epitope. Binding of aprobe antibody to antigen will form a complex consisting of a referenceantibody coupled to a bead through a capture antibody, the antigen, andthe bound probe antibody. A labelled detection antibody is added todetect bound probe antibody. Here, the detection antibody is labelledwith biotin, and bound probe antibody is detected by the interaction ofstreptavidin-PE and the biotinylated detection antibody. As shown inFIG. 1, Antibody #50 is used as the probe antibody, and the referenceantibodies are Antibody #50 and Antibody #1. Probe Antibody #50 willbind to antigen that is bound to reference Antibody #1 because theantibodies bind to different epitopes, and a labelled complex can bedetected. Probe antibody #50 will not bind to antigen that is bound byreference antibody #50 because both antibodies are competing for thesame epitope, such that no labelled complex is formed.

In this embodiment, after the incubation steps are completed, the beadsof a given well are aligned in a single file in a cuvette and one beadat a time passes through two lasers. The first laser excitesfluorochromes embedded in the beads, identifying which referenceantibody is bound to each bead. A second laser excites fluorescentmolecules bound to the bead complex, which quantifies the amount ofbound detection antibody and hence, the amount of probe antibody boundto the antigen on a reference antibody-bead complex. When a strongsignal for the detection antibody is measured on a bead, that indicatesthe reference and probe antibodies bound to that bead are bound todifferent sites on the antigen and hence, recognize different epitopeson the antigen. When a weak signal for the bound detection antibody ismeasured on a bead, that indicates the corresponding reference and probeantibodies compete for the same epitope. This is illustrated in FIG. 1.A key advantage of this embodiment is that it can be carried out inhigh-throughput mode, such that multiple competition assays can besimultaneously performed in a single well, saving both time andresources.

The assay described herein may include measurements of at least oneadditional parameter of the epitope recognition properties of primaryand secondary antibodies being characterized, for example the effect oftemperature, ion concentration, solvents (including detergent) or anyother factor of interest. One of skill in the relevant art can use thepresent disclosure to develop an experimental design that permits thetesting of at least one additional factor. If necessary, multiplereplicates of an assay may be carried out, in which factors such astemperature, ion concentration, solvent, or others, are varied accordingto the experimental design. When additional factors are tested, methodsof data analysis can be adjusted accordingly to include the additionalfactors in the analysis.

Data Analysis

Another aspect of the present invention provides processes for analyzingdata generated from at least one assay, preferably from at least onehigh throughput assay, in order to identify antibodies having similarand dissimilar epitope recognition properties. A comparative approach,based on comparing the epitope recognition properties of a collection ofantibodies, permits identification of those antibodies having similarepitope recognition properties, which are likely to compete for the sameepitope, as well as the identification of those antibodies havingdissimilar epitope recognition properties, which are likely to bind todifferent epitopes. In this way, antibodies can be categorized, or“binned” based on which epitope they recognize. A preferred embodimentprovides the Competitive Pattern Recognition (CPR) process for analyzingdata generated by a high throughput assay. More preferably, CPR is usedto analyze data generated by the Multiplexed Competitive AntibodyBinning (MCAB) high-throughput competitive assay. Application of dataanalysis processes as disclosed and claimed herein makes it possible toeliminate redundancy by identifying the distinct binding specificitiesrepresented within a pool of antigen-specific antibodies characterizedby an assay such as the MCAB assay.

A preferred embodiment of the present invention provides a process thatclusters antibodies into “bins” or categories representing distinctbinding specificities for the antigen target. In yet another preferredembodiment, the CPR process is applied to data representing the outcomesof the MCAB high-throughput competition assay in which every antibodycompetes with every other antibody for binding sites on antigenmolecules. Embodiments carried out using different data sets ofantibodies generated from XenoMouse® animals provide a demonstrationthat application of the process of the present invention producesconsistent and reproducible results.

The analysis of data generated from an experiment typically involvesmulti-step operations to normalize data across different wells in whichthe assay has been carried out and cluster data by identifying andclassifying the competition patterns of the antibodies tested. Amatrix-based computational process for clustering antibodies is thenperformed based on the similarity of their competition patterns, whereinthe process is applied to classify sets of antibodies, preferablyantibodies generated from hybridoma cells.

Antibodies that are clustered based on the similarity of theircompetition patterns are considered to bind the same epitope or similarepitopes. These clusters may optionally be displayed in matrix format,or in “tree” format as a dendrogram, or in a computer-readable format,or in any data-input-device-compatible format. Information regardingclusters may be captured from a matrix, a dendrogram or by a computer orother computational device. Data capture may be visual, manual,automated, or any combination thereof.

As used herein, the term “bin” may be used as a noun to refer toclusters of antibodies identified as having similar competitionaccording to the methods of the present invention. The term “bin” mayalso be used a verb to refer to practicing the methods of the presentinvention. The term “epitope binning assay” as used herein, refers tothe competition-based assay described herein, and includes any analysisof data produced by the assay.

Steps in data analysis are described in detail in the followingdisclosure, and practical guidance is provided by reference to the dataand results are presented in Example 2. References to the data ofExample 2, especially the matrices or dendrograms generated byperforming various data analysis steps on the input data of Example 2,serve merely as illustrations and do not limit the scope of the presentinvention in any way.

When a large number and sizes of the data sets is generated, asystematic method is needed to analyze the matrices of signalintensities to determine which antibodies have similar signal intensitypatterns. By way of example, two matrices containing m rows and mcolumns are generated in a single experiment, where m is the number ofantibodies being examined. One matrix has signal intensities for the setof competition assays in which antigen is present. The second matrix hasthe corresponding signal intensities for a negative control experimentin which antigen is absent. Each row in a matrix represents a uniquewell in a multiwell microtiter plate, which identifies a unique probeantibody. Each column represents a unique bead spectral code, whichidentifies a unique reference antibody. The intensity of signal detectedin each cell in a matrix represents the outcome of an individualcompetition assay involving a reference antibody and a probe antibody.The last row in the matrix corresponds to the well in which blockingbuffer is added instead of a probe antibody. Similarly, the last columnin the matrix corresponds to the bead spectral code to which blockingbuffer is added instead of reference antibody. Blocking buffer serves asa negative control and determines the amount of signal present when onlyone antibody (of the reference-antibody-probe-antibody pair) is present.

Similar signal intensity value patterns for two rows indicate that thetwo probe antibodies exhibit similar binding behaviors, and hence likelycompete for the same epitope. Likewise, similar signal intensitypatterns for two columns indicate that the two reference antibodiesexhibit similar binding behaviors, and hence likely compete for the sameepitope. Antibodies with dissimilar signal patterns likely bind todifferent epitopes. Antibodies can be grouped, or “binned,” according tothe epitope that they recognize, by grouping together rows with similarsignal patterns or by grouping together columns with similar signalpatterns. Such an assay described above is referred to as an epitopebinning assay.

Program to Apply Competitive Pattern Recognition (CPR) Process

One aspect of the present invention provides a program to apply the CPRprocess having two main steps: (1) normalization of signal intensities;and (2) generation of dissimilarity matrices and clustering ofantibodies based on their normalized signal intensities. It isunderstood that the term “main step” encompasses multiple steps that maybe carried as necessary, depending on the nature of the experimentalmaterial used and the nature of the data analysis desired. It is alsounderstood that additional steps may be practiced as part of the presentinvention.

Background Normalization of Signal Intensities

Input data is subjected to a series of preprocessing steps that improvethe ability to detect meaningful patterns. Preferably, the input datacomprises signal intensities stored in a two dimensional matrix, and aseries of normalization steps are carried out to eliminate sources ofnoise or signal bias prior to clustering analysis.

The input data to be analyzed comprises the results from a completeassay of epitope recognition properties. Preferably, results comprisesignal intensities measured from an assay carried out using labelledsecondary antibodies. More preferably, results using the MCAB assay areanalyzed as described herein. Two input files are generated: one inputfile from an assay in which antigen was added; and a second input filefrom an assay in which antigen was absent. The experiment in whichantigen is absent serves as a negative control allowing one to quantifythe amount of binding by the labelled antibodies that is not to theantigen. Preferably, each combination of primary antibody and secondaryantibody being tested was assayed in the presence and absence ofantigen, such that each combination is represented in both sets of inputdata. Even more preferably, the assay is carried out using theprocedures for assaying epitope recognition properties of multipleantibodies using a multi-well format disclosed elsewhere in the presentdisclosure.

The input data normally comprises signal intensities stored in a twodimensional matrix. First, the matrix corresponding to the experimentwithout antigen (negative control) experiment, A_(B), is subtracted fromthe matrix corresponding to the experiment with antigen, A_(E) to givethe background normalized matrix given by A_(N)=A_(E)−A_(B). Thissubtraction step eliminates background signal that is not due to bindingof antibodies to antigen. The above matrices are of dimension(m+1)×(m+1) where m is the number of antibodies to be clustered. Thelast row and the last column contain intensity values for experiments inwhich blocking buffer was added in place of a probe antibody orreference antibody, respectively.

In an illustrative embodiment, FIGS. 8A and 8B illustrate the intensitymatrices generated in the embodiment disclosed in Example 2, which areused as input data matrices for subsequent steps in data analysis. FIG.8A is the intensity matrix for an experiment conducted with antigen, andFIG. 8B is the intensity matrix for the same experiment conductedwithout antigen. Each row in the matrix corresponds to the signalintensities for the different beads in one well, where each wellrepresents a unique detecting antibody. Each column represents thesignal intensities corresponding to the competition of a unique primaryantibody with each of the secondary antibodies. Each cell in the matrixrepresents an individual competition assay for a different pair ofprimary and secondary antibodies. In assays of epitope recognitionproperties, addition of blocking buffer in place of one of theantibodies serves as a negative control. In the embodiment illustratedby FIGS. 8A and 8B, the last row in the matrix corresponds to the wellin which blocking buffer is added in place of a secondary antibody, andthe last column in the matrix corresponds to the beads to which blockingbuffer is added in place of primary antibody. Other arrangements ofcells within a matrix can be used to practice aspects of the presentinvention, as one of skill in the relevant art can design data matriceshaving other formats and adapt subsequent manipulations of these datamatrices to reflect the particular format chosen.

A difference matrix can be generated by subtracting the matrixcorresponding to values obtained from the experiment without antigenfrom the matrix corresponding to values obtained from the experimentwith antigen. This step is performed to subtract from the total signalthe amount of signal that is not attributed to the binding of thelabelled probe antibody to the antigen. This subtraction step generatesa difference matrix as illustrated in FIG. 9. Following thissubtraction, any antibodies that have unusually high intensities fortheir diagonal values relative to the other diagonal values are flagged.High values for a column both along and off the diagonal suggest thatthe data associated with this particular bead may not be reliable. Theantibodies corresponding to these columns are flagged at this step andare considered as individual bins.

Elimination of Background Signals Due to Nonspecific Binding:Normalization of Signal Intensities within Rows or Columns of the Matrix

In some cases, there is a significant disparity in the overall signalintensities between different rows or columns in thebackground-normalized signal intensity matrix. Row variations are likelydue to variations in intensity from well to well, while column variationis likely due to the variation in the affinities and concentrations ofdifferent probe antibodies. In accordance with one aspect of the presentinvention, there is often a linear correlation between the blockingbuffer values of the rows or columns, and the average signal intensityvalues of the rows or columns. If an intensity variation is observed, anadditional step of row and/or column normalization is performed asdescribed below.

Row normalization. Row normalization is performed when there are anysignificant well-specific signal biases, and is carried out to eliminateany “signal artifacts” that would otherwise be introduced into the dataanalysis. One of skill in the art can determine whether the step isdesirable based on the distribution of intensity values of the blockingbuffer negative controls. By way of illustration, in FIG. 2A, theblocking buffer intensity value for each row is plotted against theaverage intensity value (excluding the blocking buffer value) for thecorresponding row. The plot in FIG. 2A shows a clear linear correlationbetween the blocking buffer values and the average intensity value for arow. This figure shows that there is a well-specific signal bias in thesamples being analyzed, and that the intensity value for the blockingbuffer correlates to the overall signal intensity within a row. Thedifferent intensity biases seen in the different rows is likely due inpart to the variation in affinity for the secondary antibodies for theantigen as well as the concentration variations of these secondaryantibodies. Note that FIG. 2B shows that, for the same embodiment, thereis weaker correlation between the blocking buffer intensity values forthe columns and the average column intensity values.

For intensity variations in rows, the intensities of each row in thematrix are adjusted by dividing each value in a row by the blockingbuffer intensity value for that row. In the case where blocking bufferdata is absent, each row value is divided by the average intensity valuefor the row. In an embodiment applying the CPR process, theintensity-normalized matrix is given by

${{A_{I}\left( {i,j} \right)} = {{\frac{A_{N}\left( {i,j} \right)}{I(k)}\mspace{14mu} 1} \leq i}},{j \leq {m + 1}}$where I is a vector containing the blocking buffer or averageintensities and k=i if normalization is done with respect to rows.

Column normalization. In this final pre-processing step, each column inthe row normalized matrix (that was not flagged at the step thedifference matrix was generated) is divided by its correspondingdiagonal value. The cells along the diagonal represent competitionassays for which the primary and secondary antibodies are the same.Ideally, values along the diagonal should be small as two copies of thesame antibody should compete for the same epitope. The division of eachcolumn by its corresponding diagonal is done to measure each intensityrelative to an intensity that is known to reflect competition—i.e.,competition of an antibody against itself.

For intensity variations in columns, the intensities of each column inthe matrix are adjusted by dividing each value in a column by theblocking buffer intensity value for that row. In the case where blockingbuffer data is absent, each column value is divided by the averageintensity value for the column. In an embodiment applying the CPRprocess, the intensity-normalized matrix is given by

${{A_{I}\left( {i,j} \right)} = {{\frac{A_{N}\left( {i,j} \right)}{I(k)}\mspace{14mu} 1} \leq i}},{j \leq {m + 1}}$where I is a vector containing the blocking buffer or averageintensities and k=j if normalization is done with respect to columns.

Setting threshold values prior to row or column normalization. Toprevent artificial inflation of low signal values in this normalizationstep, all blocking buffer values that are below a minimum user-definedthreshold value are flagged and then adjusted to the user-definedthreshold value which represents the lowest reliable signal intensityvalue, prior to row or column division. This threshold is set based on ahistogram of the signal intensities. This normalization step adjusts forvariations in intensity from well to well.

By way of example, FIG. 17 illustrates an adjusted difference matrix forthe data of Example 2, wherein the minimum reliable signal intensity isset to 200 intensity units. Each row in the matrix is adjusted bydividing it by the last intensity value in the row. As noted above, thelast intensity value in each row corresponds to the intensity value forbeads to which blocking buffer is added in place of primary antibody.This step adjusts for the well-to-well variation in intensity valuesacross the row. FIG. 18 illustrates a row normalized matrix for the dataof Example 2.

Further by way of example, FIG. 2A presents data from an embodiment inwhich the blocking buffer intensity value for each row was plottedagainst the average intensity value for the corresponding row. This plotshows a linear correlation between the blocking buffer values and theaverage intensity value for a row, and suggests that there arewell-specific intensity biases. These biases may be partially due to thevariation in affinity for the probe antibodies for the antigen and theconcentration variations of the probe antibodies. FIG. 2B presents datafrom an embodiment in which the blocking buffer intensity value for eachcolumn was plotted against the average intensity value for thecorresponding column.

In another illustrative embodiment, FIG. 2C shows a scatter plot of thebackground-normalized difference matrix intensities plotted against theintensities for the matrix of results from an embodiment using antigen.This plot shows a tight linear correlation (slope=1) for signal valuesgreater than 1000, and a more scattered correlation for lower signalvalues. The points in FIG. 2C are shaded according to the value of afraction calculated as the subtracted signal divided by the signal forthe experiment with antigen present. Smaller fraction values (closer tozero) correspond to high background contribution and have light shadingin FIG. 2C. Larger fraction values (closer to 1) correspond to lowerbackground contribution and have darker shading. In FIG. 2C, the smallerfraction values are predominantly in the lower-left region of thescatter plot, suggesting that the contribution of background becomesless for subtracted signal values greater than 1000.

The plot shown in FIG. 2C suggests that for this embodiment, intensityvalues of the background-normalized matrix greater than 1000 have a lowbackground signal contribution relative to the signal due to antigenbinding. These matrix cells likely correspond to antibody pairs that donot compete for the same epitope. Conversely, intensity values below1000 likely correspond to antibody pairs that bind to the same epitope.In accordance with one aspect of the present invention, it is expectedthat the intensity values along the diagonal would be small, asidentical reference and probe antibodies compete for the same epitope.In the embodiment illustrated in FIG. 2C, all but one of the diagonalvalues of the background-normalized signal intensity matrix haveintensity values below 1000.

Normalization of Signal Intensities Relative to the Baseline Signal forProbe Antibodies

In a final step, data are adjusted by dividing each column or row by itscorresponding diagonal value to generate the final normalized matrixgiven by

${A_{F}\left( {i,j} \right)} = {\frac{A_{I}\left( {i,j} \right)}{A_{I}\left( {j,j} \right)}.}$

Once again, to prevent artificial inflation of low signal values in thisnormalization step, all diagonal values below a minimum user-definedthreshold value are adjusted to the threshold value before the diagonaldivision is done. This step is done for all columns or rows, exceptthose that have diagonal values that are significantly high relative toother values in the column or row. This step normalizes each intensityvalue relative to the intensity corresponding to the individualcompetition assay for which the reference and probe antibodies are thesame. This intensity value should be low and ideally reflect thebaseline signal intensity value for the column or row, because twoidentical antibodies should compete for the same epitope and hence beunable to simultaneously bind to the same antigen. Columns havingunusually large diagonal values are identified as outliers and excludedfrom the analysis. High-diagonal-intensity values may indicate that theantigen has two copies of the same epitope, e.g., when the antigen is ahomodimer.

Pattern Recognition Analysis: Dissimilarity Matrices

In accordance with another aspect of the present invention, a secondstep in data analysis involves generating a dissimilarity matrix fromthe normalized intensity matrix in two steps. First, the normalizedintensity values that are below a user-defined threshold value forbackground are set to zero (and hence represent competition) and theremaining values are set to 1, indicating that the antibodies bind totwo different epitopes. Accordingly, intensity values that are less thanthe intensity equal to this threshold multiplied by the intensity valueof the diagonal value are considered low enough to represent competitionfor the same epitope by the antibody pair. The dissimilarity matrix ordistance matrix for a given threshold value is computed from the matrixof zeroes and ones by determining the number of positions in which eachpair of rows differs. The entry in row i and column j, corresponds tothe fraction of the total number of primary antibodies that differ intheir competition patterns with the secondary antibodies represented inrows i and j.

By way of example, FIG. 14 shows the number of positions (out of 22total) at which the patterns for any two antibodies differ. In thisembodiment, dissimilarities are computed with respect to rows instead ofcolumns because the row intensities have already been adjusted forwell-specific intensity biases and therefore the undesirable effects ofunequal secondary antibody affinities and concentrations have beenfactored out. In addition, the concentrations and affinities of primaryantibodies are consistent between rows. However, for the columns, thereis not an apparent consistent trend between average intensity andbackground intensity which suggests that there is not an obvious way tofactor out the undesirable affects of the variable primary antibodyconcentrations and affinities. Therefore, comparing the signals betweencolumns might be less valid.

Dissimilarity matrix using CPR. In an embodiment applying the CPRprocess, a threshold matrix, A_(T), of zeros and ones is generated asdescribed below. Normalized values that are less than or equal to athreshold value are set to zero to indicate that the corresponding pairsof antibodies compete for the same epitope. The threshold matrix isgiven by

${A_{T}\left( {i,j} \right)} = \left\{ \begin{matrix}{{0\mspace{14mu}{if}\mspace{14mu}{A_{F}\left( {i,j} \right)}} \leq T} \\{{1\mspace{14mu}{if}\mspace{14mu}{A_{F}\left( {i,j} \right)}} > {T.}}\end{matrix} \right.$

The remaining normalized intensity values are set to one, and the valuesrepresent pairs of antibodies that bind to different epitopes.

The dissimilarity matrix is computed from the threshold matrix bysetting the value in the i^(-th) row and j^(-th) column of thedissimilarity matrix to the fraction of the positions at which two rows,i and j of the matrix of zeros and ones, differ. A dissimilarity matrixfor a specified threshold value, T, is given by

${D_{T}\left( {i,j} \right)} = \frac{m - {N_{1}\left( {i,j} \right)}}{m}$where N₁ is the number of ones (1s) present when the i^(-th) and j^(-th)rows are summed.

By way of example, for the matrix shown in Table 1 below, thedissimilarity value corresponding to the first and second rows is 0.4,because the number of positions at which the two rows differ is 2 out of5. For an ideal experiment, the dissimilarity matrix that is generatedbased on a comparison of rows of the original signal intensity matrix,should be the same as the dissimilarity matrix that is generated basedon the comparison of columns.

TABLE 1 Matrix Used to Compute Dissimilarity Values A B C D E A 0 1 1 10 B 1 1 1 0 0 C 1 1 1 1 1 D 1 1 1 0 1 E 1 0 1 1 0

Effect of Calculating Dissimilarity Matrices at Multiple ThresholdValues

If desired, the process of generating dissimilarity matrices is repeatedfor background threshold values incremented inclusively between twouser-defined threshold values which represent lower and upper thresholdvalues for intensity (where the threshold value is as described above).The dissimilarity matrices generated over a range of backgroundthreshold values is averaged and used an input to the clusteringalgorithm. The process of averaging over several thresholds is performedto minimize the sensitivity of the final dissimilarity matrix to any oneparticular choice for the threshold value. The effect of variation ofthe threshold value on the apparent dissimilarity is illustrated by FIG.4, which shows the fraction of dissimilarities for a pair of antibodies(2.1 and 2.25) as a function of the threshold value for threshold valuesranging between 1.5 and 2.5. As the threshold value changes from 1.8 to1.9 the amount of dissimilarity between the signal patterns for the twoantibodies changes substantially from 15% to nearly 0%. This figureshows how the amount of dissimilarity between the signal patterns for apair of antibodies may be sensitive to one particular choice for acutoff value, as it can vary substantially for different thresholdvalues. The sensitivity is mitigated by taking the average dissimilarityvalue over a range of different threshold values.

Calculating dissimilarity matrices at multiple threshold values usingCPR. In a preferred embodiment, the process of computing dissimilaritymatrices using CPR is repeated for several incremental threshold valueswithin a user-defined range of values. The average of thesedissimilarity matrices is computed and used as input to the clusteringstep where the average is computed as

${D_{Ave}\left( {i,j} \right)} = \frac{\sum\limits_{T}{D_{T}\left( {i,j} \right)}}{N_{T}}$where N_(T) is the number of different thresholds to be averaged.

This process of averaging over several thresholds is done to minimizethe sensitivity of the dissimilarity matrix to a particular cutoff valuefor the threshold.

Dissimilarity Matrices from Multiple Experiments

If there are input data sets for more than one experiment, normalizedintensity matrices are first generated as described above for eachindividual experiment. Normalized values above a threshold value(typically set to 4) are then set to this threshold value. Setting thehigh-intensity values to the threshold value is done to prevent anysingle intensity value from having too much weight when the averagenormalized intensity values are computed for that cell. The averageintensity matrix is computed by taking individual averages over all datapoints for each antibody pair out the group consisting of antibodiesthat are in at least one of the input data sets. Antibody pairs forwhich there are no intensity values are flagged. The generation of thedissimilarity matrix is as described above with the exception that theentry in row i and column j corresponds to the fraction of the positionsat which two rows, i and j differ out of the total number of positionsfor which both rows have an intensity value. If the two rows have nosuch positions, then the dissimilarity value is set arbitrarily high andflagged.

Clustering of Antibodies Based on Their Normalized Signal Intensities

Another aspect of the present invention provides processes forclustering antibodies based on their normalized signal intensities,using various computational approaches to identify underlying patternsin complex data. Preferably, any such process utilizes computationalapproaches developed for clustering points in multidimensional space.These processes can be directly applied to experimental data todetermine epitope binding patterns of sets of antibodies by regardingthe signal levels for the n² competition assays of n probe antibodies inn sampled reference antibodies as defining n points in n-dimensionalspace. These methods can be directly applied to epitope binning byregarding the signal levels for the competition assays of each secondaryantibody with all of the n different primary antibodies as defining apoint in n-dimensional space.

Results of clustering analysis can be expressed using visual displays.In addition or in the alternative, the results of clustering analysiscan be captured and stored independently of any visual display. Visualdisplays are useful for communicating the results of an epitope binningassay to at least one person. Visual displays may also be used as ameans for providing quantitative data for capture and storage. In onepreferred embodiment, clusters are displayed in a matrix format andinformation regarding clusters is captured from a matrix. Cells of amatrix can have different intensities of shading or patterning toindicate the numerical value of each cell; alternately, cells of amatrix can be color-coded to indicate the numerical value of each cell.In another preferred embodiment, clusters are displayed as dendrogramsor “trees” and information regarding clusters is captured from adendrogram based on branch length and height (distance) of branches. Inyet another preferred embodiment, clusters are identified by automatedmeans, and information regarding clusters is captured by an automateddata analysis process using a computer or any data input device.

One approach that has proven valuable for the analysis of largebiological data sets is hierarchical clustering (Eisen et al. (1998)Proc. Natl. Acad. Sci. USA 95:14863-14868). Applying this method,antibodies can be forced into a strict hierarchy of nested subsets basedon their dissimilarity values. In an illustrative embodiment, the pairof antibodies with the lowest dissimilarity value is grouped togetherfirst. The pair or cluster(s) of antibodies with the next smallestdissimilarity (or average dissimilarity) value is grouped together next.This process is iteratively repeated until one cluster remains. In thismanner, the antibodies are grouped according to how similar theircompetition patterns are, compared with the other antibodies. In oneembodiment, antibodies are grouped into a dendrogram (sometimes called a“phylogenetic tree”) whose branch lengths represent the degree ofsimilarity between the binding patterns of the two antibodies. Longbranch lengths between two antibodies indicate they likely bind todifferent epitopes. Short branch lengths indicate that two antibodieslikely compete for the same epitope.

In a preferred embodiment, the antibodies corresponding to the rows inthe matrix are clustered by hierarchical clustering based on the valuesin the average dissimilarity matrix using an agglomerative nestingsubroutine incorporating the Manhattan metric with an inputdissimilarity matrix of the average dissimilarity matrix. In anespecially preferred embodiment, antibodies are clustered byhierarchical clustering based on the values in the average dissimilaritymatrix using the SPLUS 2000 agglomerative nesting subroutine using theManhattan metric with an input dissimilarity matrix of the averagedissimilarity matrix. (SPLUS 2000 Statistical Analysis Software,Insightful Corporation, Seattle, Wash.)

In accordance with another aspect of the present invention, the degreeof similarity between two dendrograms provides a measure of theself-consistency of the analyses performed by a program applying the CPRprocess. A non-limiting theory regarding similarity and consistencypredicts that a dendrogram generated by clustering rows and a dendrogramgenerated by clustering columns of the same background-normalized signalintensity matrix should be identical, or nearly so, because: if Antibody#1 and Antibody #2 compete for the same epitope, then the intensityshould be low when Antibody #1 is the reference antibody and Antibody #2is the probe antibody, as well as when Antibody #2 is the referenceantibody and Antibody #1 is the probe antibody. Likewise, when the twoantibodies bind to different epitopes, the intensities should beuniformly high. By this reasoning, the degree of similarity between tworows of the signal intensity matrix should be the same as between twocolumns of the similarity matrix. A high level of self-consistencybetween row clustering and column clustering suggests that, for a givenexperiment, the experimental protocol described herein, practiced withthe program for applying the process of the present invention, producesrobust results.

In accordance with a further aspect of the present invention, the degreeof overlap between two epitopes may also be inferred based on thelengths of the longest branches connecting clusters in a dendrogram. Forexample, if a target antigen has two distinct, completely nonoverlappingepitopes, then one would expect that an antibody binding to one of theepitopes would have an opposite signal intensity pattern from anantibody binding to another epitope. According to this reasoning, if thebinding sites are nonoverlapping, the signal patterns for the set ofantibodies binding one epitope should be completely anticorrelated tothe signal pattern for the set of antibodies recognizing the otherepitope. Hence, dissimilarity values that are close to one (1) for twodifferent clusters suggest that the corresponding epitopes do notinterfere with each other or overlap in their binding sites on theantigen.

The embodiment described in Example 2 below demonstrates how clusteringresults can be displayed as a dendrogram (FIG. 5) or in matrix form(FIGS. 16 and 17). The data points (values of antibodies against theANTIGEN14 target) were grouped into a dendrogram whose branch lengthsrepresent the degree of similarity between two antibodies, where thedendrogram was generated using the Agglomerative Nesting module of theSPLUS 2000 statistical analysis software. To facilitate comparison. InFIGS. 16 and 17, the order of the antibodies in rows and columns of thematrices is the same as the order of the antibodies as displayed fromleft to right under the dendrogram in FIG. 5. The individual cells arevisually coded by assigning a fill pattern to cells according to theirnumerical value. In FIG. 16, cells with values below a lower thresholdvalue have forward hatching. Cells with values below a lower thresholdand an upper threshold have no fill pattern. Cells with values above theupper threshold have stippling. A block having cells that have no fillpattern or have forward hatching indicates that all of the antibodiescorresponding to that block that recognize the same epitope. Cells withstippling correspond to antibodies that recognize different epitopes. InFIG. 17, the cells are the normalized intensity values and are alsovisually coded according to their value. Cells that have forwardhatching have intensities below a lower threshold, cells with no fillpattern have intensities between a lower and an upper threshold, whilecells with back hatching have intensities above an upper threshold. Acell with forward hatching indicates the antibodies in its correspondingrow and column compete for the same epitope (as the intensity is low). Acell with back hatching corresponds to a higher intensity and isindicative that the antibodies in the corresponding row and column bindto different epitopes.

The results from this illustrative embodiment (Example 2) indicate thatthe processes of the present invention provide a high level ofself-consistency for the data with regard to revealing whether or nottwo antibodies compete for the same epitope. The symmetry of the fillpatterns in FIGS. 16 and 17 with respect to the diagonal clearly showsthis self-consistency. The reason is that the antibodies in row A andcolumn B are the same pair as in row B and column A. Hence, if the pairof antibodies compete for the same epitope, then the intensity should below both when antibody A is the primary antibody and antibody B is thesecondary antibody, as well as when antibody B is the primary antibodyand antibody B is the secondary antibody. Therefore, the intensity forthe cell of the ith row and jth column as well that for the jth row andith column should both be low. Likewise, if these two antibodiesrecognize different epitopes, then both corresponding intensities shouldbe high. Out of the approximately 200 pairs of cells in FIG. 17, onlyone pair showed a discrepancy where one member of the pair had anintensity below 1.5 while the other member had an intensity above 2.5.The level of self-consistency of the resulting normalized matricesproduced by the algorithm provides a measure of the reliability of boththe data generated as well as the algorithm's analysis of the data. Thehigh level of self-consistency for the data set (over 99%) of antibodiesagainst the ANTIGEN14 target suggest that the data analysis processesdisclosed and claimed herein generate reliable results.

Clustering Antibodies from Multiple Experiments

Another aspect of the present invention provides a method for combiningdata sets to overcome limitations of experimental systems used to screenantibodies. By performing multiple experiments in which each experimenthas at least x antibodies in common with each other experiment, andproviding the multiple resulting data sets as input to the clusteringprocess, it should be possible to reliably cluster very large numbers ofantibodies. By having a set of m antibodies in common between the mexperiments, it becomes possible to infer which cluster antibodies arelikely to belong to even if they are not tested against every otherantibody. This suggests that using this method for data analysis withmultiple data sets, it may be possible to achieve an even higherthroughput with fewer assays

By way of example, the Luminex technology provides 100 uniquefluorochromes, so it is possible to study 100 antibodies at most in asingle experiment. The consistency of results produced by the clusteringstep for individual data sets and the combined data set indicate that itis possible to infer which epitope is recognized by which antibody, evenif the epitope and/or antibody are not tested against every otherantibody. In a preferred embodiment, the CPR process can be used tocharacterize the binding patterns of more than 100 antibodies byperforming multiple experiments using overlapping antibody sets. Bydesigning experiments in such a way that each experiment has a set ofantibodies in common with the other experiments, the combined-averagematrix will not have any missing data.

A further aspect provides that the results of data analysis for a givenset of antibodies are useful to aid in the rational design of subsequentexperiments. For example, if a data set for a first experiment showswell-defined clusters emerging, then the set of antibodies for a secondexperiment should include representative antibodies from the first setof antibodies as well as untested antibodies. This approach ensures thateach set of antibodies has sufficient material to define the twoepitopes, and that the sets overlap sufficiently to permit comparisonbetween sets. By comparing the competition patterns of an untested setof antibodies in the second experiment with a sample set of knownantibodies from the first experiment, it should be possible to determinewhether or not the untested antibodies recognize the same epitope(s) asdo the first set of antibodies. This overlapping experimental designpermits reliable comparison of the competition patterns of the first setwith the second set of antibodies, to determine whether the antibodiesin the second experiment recognize existing epitopes, or whether theyrecognize one or more completely novel epitopes. Further, experimentscan be iteratively designed in an optimal way, so that multiple sets ofantibodies can be tested against existing and new clusters.

Analysis of Data from Multiple Experiments

Results from the embodiment described in Example 3 below, usingantibodies against the ANTIGEN39 target, demonstrate that the processesdisclosed and claimed herein are suitable for analyzing data frommultiple experiments. In this embodiment, ANTIGEN39 antibodies weretested for binding to cell surface ANTIGEN39 antigen, where ANTIGEN39antigen is a cell surface protein. First, normalized intensity matriceswere generated for each individual experiment, wherein normalized valuesabove a selected threshold value are set to the selected threshold valueto prevent any single normalized intensity value from having too muchinfluence on the average value for that antibody pair. A singlenormalized matrix was generated from the individual normalized matricesby taking the average of the normalized intensity values over allexperiments for each antibody pair for which data was available. Then asingle dissimilarity matrix was generated as described above, with theexception that the fraction of the positions at which two rows, i and jdiffer only considers the number of positions for which both rows havean intensity value.

For five experiments using ANTIGEN39 antibodies, the clustering resultsfor the five input data sets showed that there were a large number ofclusters of varying degree of similarity, suggesting the presence ofseveral different epitopes, some of which may overlap. This is shown inFIG. 6A, FIG. 18, FIG. 19, and FIG. 30. For example, the clustercontaining antibodies 1.17, 1.55, 1.16, 1.11, and 1.12 and the clustercontaining 1.21, 2.12, 2.38, 2.35, and 2.1 are fairly closely related,as each antibody pair shows no more than 25% difference, with theexception of 2.35 and 1.11. This high degree of similarity across thetwo clusters suggested that the two different epitopes may have a highdegree of similarity

The five data sets from separate experiments using ANTIGEN39 antibodieswere also independently clustered, to demonstrate that the processesdisclosed and claimed herein produce consistent clustering results.Clustering results are summarized in FIGS. 6B-6F and in FIGS. 20-30,where FIG. 30 summarizes the clusters for each of the individual datasets and for the combined data set with all of the antibodies for thefive experiments. FIG. 6B shows the dendrogram for the ANTIGEN39antibodies for Experiment 1: Antibodies 1.12, 1.63, 1.17, 1.55, and 2.12consistently clustered together in this experiment as well as in otherexperiments as do antibodies 1.46, 1.31, 2.17, and 1.29. FIG. 6C showsthe dendrogram for the ANTIGEN39 antibodies for Experiment 2: Antibodies1.57 and 1.61 consistently clustered together in this experiment as wellas in other experiments.

FIG. 6D shows the dendrogram for the ANTIGEN39 antibodies for Experiment3: Antibodies 1.55, 1.12, 1.17, 2.12, 1.11, and 1.21 consistentlyclustered together in this experiment as well as in other experiments.FIG. 6E shows the dendrogram for the ANTIGEN39 antibodies for experiment4: Antibodies 1.17, 1.16, 1.55, 1.11, and 1.12 consistently clusteredtogether in this experiment as well as in other experiments as doantibodies 1.31, 1.46, 1.65, and 1.29, as well as antibodies 1.57 and1.61. FIG. 6F shows the dendrogram for the ANTIGEN39 antibodies forexperiment 5: Antibodies 1.21, 1.12, 2.12, 2.38, 2.35, and 2.1consistently clustered together in this experiment as well as in otherexperiments.

In general, the clustering algorithm produced consistent results bothamong the individual experiments and between the combined and individualdata sets. Antibodies which cluster together or are in neighboringclusters for multiple individual data sets also cluster together or bein neighboring clusters for the combined data set. For example, cellshaving lighter shading indicate antibodies that consistently clusteredtogether in the combined data set and in all of the data sets in whichthey were present (Experiments 1, 3, 4, and 5). These results indicatethat the algorithm produces consistent clustering results both acrossmultiple individual experiments and that it retains the consistency uponthe merging of multiple data sets.

Finally, there is a high level of self-consistency for the data withregard to revealing whether or not two antibodies compete for the sameepitope. The percent of antibody pairs for which the data consistentlyreveals whether or not they compete for the same epitope is summarizedfor each data set in Table 2, below, which reveals that the consistencywas nearly 90% for four out of the five individual data sets as well asfor the combined data set.

TABLE 2 Percent Consistency Values for ANTIGEN39 Antibody ExperimentsExperiment % Consistency 1 92 2 82 3 88 4 92 5 88 Combined 88

Consistency of Epitope Binning Results with Flow Cytometry (FACS)Results

Results from the embodiment described in Example 3 below, usingantibodies against the ANTIGEN39 target further demonstrate that resultsgenerated by epitope binning according to the methods of the presentinvention are consistent with the results generated using flow cytometry(fluorescence-activated cell sorter, FACS). Cells expressing ANTIGEN39were sorted by FACS, and ANTIGEN39-negative cells were used as negativecontrols also sorted by FACS. The cell surface binding sites recognizedby antibodies from different bins represent different epitopes. FIG. 3shows a comparison of results from antibody experiments using theanti-ANTIGEN39 antibody, with results using FACS. As shown in FIG. 3,the antibodies in a given bin are either all positive (Bins 1, 4, 5) orall negative (bins 2 and 3) in FACS, which indicates that the antibodyepitope binning assay indeed bins antibodies based on their epitopebinding properties. Thus, epitope binning, as described herein, providesan efficient, rapid, and reliable method for determining the epitoperecognition properties of antibodies, and sorting and categorizingantibodies based on the epitope they recognize.

Alternative Data Analysis Process and Consistency of Epitope Binningwith Sequence Results.

An alternative data analysis process involves subtracting the datamatrix for the experiment carried out with antigen from the data matrixfor the experiment without antigen to generate a normalized backgroundintensity matrix. The value in each diagonal cell is then used as abackground value for determining the binding affinity of the antibody inthe corresponding column. Cells in each column the normalized backgroundintensity matrix (the subtracted matrix) having values significantlyhigher than the value of the diagonal cell for that column arehighlighted or otherwise noted. Generally, a value of about two timesthe corresponding diagonal is considered “significantly higher”,although one of skill in the art can determine what increase overbackground is the threshold for “significantly higher” in a particularembodiment, taking into account the reagents and conditions used, andthe “noisiness” of the input data. Columns with similar binding patternsare grouped as a bin, and minor differences within the bin areidentified as sub-bins. This data analysis can be carried outautomatically for a given set of input data. For example, input data canbe stored in a computer database application where the cells in diagonalare automatically marked, and the cells in each column as compared withthe numbers in diagonal are highlighted, and columns with similarbinding patterns are grouped.

In a preferred embodiment using fifty-two (52) antibodies againstANTIGEN54, binning results using the data analysis process describedabove correlated with sequence analysis the CDR regions of antibodiesbinned using the MCAB competitive antibody assay. The 52 antibodiesconsisted of 2 or 3 clones from 20 cell lines. As expected, sequences ofclones from same line were identical, so only one representative clonefrom each line was sequenced. The correspondence between the epitopebinning results and sequence analysis of antibodies binned by thismethod indicates this approach is suitable for identifying antibodieshaving similar binding patterns. In addition, correspondence between theepitope binning results and sequence analysis of antibodies binned bythis method means that the epitope binning method provides informationand guidance about which antibody sequences are important in determiningthe epitope specificity of antibody binding.

EXAMPLES Example 1 Assay of Epitope Recognition Properties

Generation and Preliminary Characterization of Antibodies.

Hybridoma supernatants containing antigen-specific human IgG monoclonalantibodies used for binning were collected from cultured hybridoma cellsthat had been transferred from fusion plates to 24-well plates.Supernatant was collected from 24-well plates for binning analysis.Antibodies specific for the antigen of interest were selected byhybridoma screening, using ELISA screening against their antigens.Antibodies positive for binding to the antigen were ranked by theirbinding affinity through a combination of a 96-well plate affinityranking method and BIAcore affinity measurement. Antibodies with highaffinity for the antigen of interest were selected for epitope binningThese antibodies will be used as the reference and probe test antibodiesin the assay.

Assay Using Luminex Beads

First, the concentration of mouse anti-human IgG (mxhIgG) monoclonalantibodies used as capture antibody to capture the reference antibodywas measured, and mxhIgG antibodies were dialyzed in PBS to removeazides or other preservatives that could interfere with the couplingprocess. Then the mxhIgG antibodies were coupled to Luminex beads(Luminex 100 System, Luminex Corp., Austin Tex.) according tomanufacturer's instructions in the Luminex User Manual, pages 75-76.Briefly, mxhIgG capture antibody at 50 μg/ml in 500 μl PBS was combinedwith beads at 1.25×10⁷ beads/ml in 300 μl. After coupling, beads werecounted using a hemocytometer and the concentration was adjusted to1×10⁷ beads/ml.

The antigen-specific antibodies were collected and screened as describedabove, and their concentrations were determined. Up to 100 antibodieswere selected for epitope binning. The antibodies were diluted accordingto the following formula for linking the antibodies to up to 100uniquely labelled beads to form labelled reference antibodies:

Total volume of the samples in each tube: Vt=(n+1)×100 μl+150 μl,

where n=total number of samples including controls.

Volume of individual sample needed for dilution: Vs=C×Vt/Cs,

Cs=IgG concentration of each sample. C=0.2-0.5 μg/ml.

Samples were prepared according to the above formula, and 150 μl of eachdiluted sample containing a reference antibody was aliquotted into awell of a 96-well plate. Additional aliquots were retained for use as aprobe antibody at a later stage in the assay. The stock ofmxhIgG-coupled beads was vortexed and diluted to a concentration of 2500of each bead per well or 0.5×10⁵/ml. The reference antibodies wereincubated with mxhIgG-coupled beads on a shaker in the dark at roomtemperature overnight.

A 96-well filter plate was pre-wetted by adding 200 μl wash buffer andaspirating. Following overnight incubation, beads (now with referenceantibodies bound to mxhIgG bound to beads) were pooled, and 100 μl wasaliquotted into each well of a 96-well microtiter filter plate at aconcentration of 2000 beads per well. The total number of aliquots ofbeads was twice the number of samples to be tested, thereby permittingparallel experiments with and without antigen. Buffer was immediatelyaspirated to remove any unbound reference antibody, and beads werewashed three times.

Antigen was added (50 μl) to one set of samples; and beads wereincubated with antigen at a concentration of 1 μg/ml for one hour. Abuffer control was added to the other set of samples, to provide anegative control without antigen.

All antibodies being used as probe antibodies were then added to allsamples (with antigen, and without antigen). In this experiment, eachantibody being used as a reference antibody was also used as a probeantibody, in order to test all combinations. The probe antibody shouldbe taken from the same diluted solution as the reference antibody, toensure that the antibody is used at the same concentration. Probeantibody (50 μl/well) was added to all samples and mixtures wereincubated in the dark for 2 hours at room temperature on a shaker.Samples were washed three times to remove unbound probe antibody.

Detection antibody: Biotinylated mxhIgG (50 μl/well) was added at a1:500 dilution, and the mixture was incubated in the dark for 1 hour ona shaker. Beads were washed three times to remove unbound BiotinylatedmxhIgG. Streptavidin-PE at 1:500 dilution was added, 50 μl/well. Themixture was incubated in the dark for 15 minutes at room temperature ona shaker, and then washed three times to remove unbound components.

In accordance with manufacturer's instructions, the Luminex 100 and XYPbase were warmed up using Luminex software. A new session was initiated,and the number of samples and the designation numbers of the beads usedin the assay were entered.

Beads in each well were resuspended in 80 μl dilution buffer. The96-well plate was placed in the Luminex based and the fluorescenceemission spectrum of each well was read and recorded.

Optimization of Assay

To optimize the assay, the Luminex User's Manual Version 1.0 wasinitially used for guidance regarding the concentrations of beads,antibodies, and incubation times. It was determined empirically that alonger incubation time provided assured binding saturation and was moresuitable for the nanogram antibody concentrations used in the assay.

Example 2 Analysis of a Single Data Set: ANTIGEN14 Antibodies

Data Input

Antibodies were assayed as described in Example 1, and results werecollected. Input files consisted of input matrices shown in FIG. 8A(antigen present) and FIG. 8B (antigen absent) for a data setcorresponding to a single experiment for the ANTIGEN14 target.

Normalization of ANTIGEN14 Target Data

First, the matrix corresponding to the experiment without antigen(negative control, FIG. 8B) experiment was subtracted from the matrixcorresponding to the experiment with antigen (FIG. 8A), to eliminate theamount of background signal due to nonspecific binding of the labelledantibody. The difference between the two matrices is shown in FIG. 9.The column corresponding to antibody 2.42 has unusually large valuesboth on and off the diagonal and was flagged and treated separately inthe data analysis as described above.

Row Normalization

The difference matrix was adjusted by setting values below theuser-defined threshold value of 200 to this threshold value as shown inFIG. 10. This adjustment was done to prevent significant artificialinflation of low signal values in subsequent normalization steps (asdescribed above). The intensities of each row in the matrix were thennormalized by dividing each row value by the row value corresponding toblocking buffer (FIG. 11). This adjusts for the well-to-well intensityvariation as discussed above and illustrated in FIG. 2A.

Column Normalization

All columns except the one corresponding to antibody 2.42 werecolumn-normalized as described above and are shown in FIG. 12.

Dissimilarity Matrix

A dissimilarity (or distance) matrix was generated in a multistepprocedure. First, intensity values below the user-defined threshold (setto two times the diagonal intensity values) were set to zero and theremaining values were set to one (FIG. 13). This means that intensityvalues that are less than twice the intensity value of the diagonalvalue are considered low enough to represent competition for the sameepitope by the antibody pair. The dissimilarity matrix is generated fromthe matrix of zeroes and ones by setting the entry in row i and column jto the fraction of the positions at which two rows, i and j differ. FIG.14 shows the number of positions (out of 22 total) at which the patternsfor any two antibodies differed for the set of antibodies generatedagainst the ANTIGEN14 target.

A dissimilarity matrix was generated from the matrix of zeroes and onesgenerated from each of several threshold values ranging from 1.5 to 2.5(times the values of the diagonals), in increments of 0.1. The averageof these dissimilarity matrices was computed (FIG. 15) and used as inputto the clustering algorithm. The significance of taking the average ofseveral dissimilarity matrices is illustrated in FIG. 4. FIG. 4 showsthe fraction of dissimilarities for a pair of antibodies (2.1 and 2.25)as a function of the threshold value for threshold values ranging from1.5 to 2.5. As the threshold value changed from 1.8 and 1.9 the amountof dissimilarity between the signal patterns for the two antibodieschanged substantially from 0% to nearly 15%. This figure shows how theamount of dissimilarity between the signal patterns for a pair ofantibodies may be sensitive to one particular choice of cutoff value, asit can vary substantially for different threshold values.

Clustering

Hierarchical clustering. Using the Agglomerative Nesting Subroutine inSPLUS 2000 statistical analysis software, antibodies were grouped (orclustered) using the average dissimilarity matrix described above asinput. In this algorithm, antibodies were forced into a strict hierarchyof nested subsets. The pair of antibodies with the smallestcorresponding dissimilarity value in the entire matrix is groupedtogether first. Then, the pair of antibodies, or antibody-cluster, withthe second smallest dissimilarity (or average dissimilarity) value isgrouped together next. This process was iteratively repeated until onecluster remained.

Visualizing Clusters in Dendrograms

The dendrogram calculated for the ANTIGEN14 target is shown in FIG. 5.The length (or height) of the branches connecting two antibodies isinversely proportional to the degree of similarity between theantibodies it binds. This dendrogram shows that there were two verydistinct epitopes recognized by these antibodies. One epitope wasrecognized by antibodies 2.73, 2.4, 2.16, 2.15, 2.69, 2.19, 2.45, 2.1,and 2.25. A different epitope was recognized by antibodies 2.13, 2.78,2.24, 2.7, 2.76, 2.61, 2.12, 2.55, 2.31, 2.56, and 2.39. Antibody 2.42does not have a pattern that was very similar to any other antibody buthad some noticeable similarity to the second cluster, indicating that itmay recognize yet a third epitope which partially overlaps with thesecond epitope.

Visualizing Clusters in Matrices

This clustering of these antibodies can also be seen in FIG. 16 and FIG.17. In FIG. 16 the rows and columns of the dissimilarity matrix wererearranged according to the order of the “leaves” or clades on thedendrogram and the individual cells were visually coded according to thedegree of dissimilarity. Cells that have forward hatching correspond toantibody pairs that were very similar (less than 10% dissimilar). Cellsthat have no fill pattern correspond to those antibodies that werefairly similar (between 10% and 25% dissimilar). Cells that havestrippling correspond to antibody pairs that were more than 25%dissimilar. The forward hatched blocks correspond to different clustersof antibodies. Excluding the blocking buffer, there appeared to be two,or possibly three, blocks corresponding to the groups of antibodiesmentioned above. FIG. 16 also shows that, allowing for a slightly highertolerance for dissimilarity, Antibody 2.42 can be considered a member ofthe second cluster.

In FIG. 17, the rows and columns of the normalized intensity matrix wererearranged according to the order of the leaves on the dendrogram andthe individual cells were visually coded according to their normalizedintensity values. Cells that have forward hatching correspond toantibody pairs that had a high intensity (at least 2.5 times greaterthan the background). Cells that have no fill pattern had an intensitybetween 1.5 and 2.5 times the background. Cells that have forwardhatching correspond to intensities that were less than 1.5 times thebackground. When comparing the visual markings of the rows of thismatrix, two very distinct patterns emerged corresponding to the twoepitopes shown above. Furthermore, note that the visual coding is verysymmetric with respect to the diagonal. This shows that there was a highlevel of self-consistency for the data with regard to revealing whethertwo antibodies compete for the same epitope. The reason is that ifantibody A and antibody B compete for the same epitope, then theintensity should be low both when antibody A is the primary antibody andantibody B is the secondary antibody, as well as when antibody B is theprimary antibody and antibody B is the secondary antibody. Therefore,the intensity for the cell of the i^(-th) row and j^(-th) column as wellthat for the j^(-th) row and i^(-th) column should both be low.Likewise, if these two antibodies recognized different epitopes, thenboth corresponding intensities should have been high. Out of theapproximately 200 pairs of cells, for only one pair did one member ofthe pair have an intensity below 1.5 while the other member had anintensity above 2.5. The level of self-consistency of the resultingnormalized matrices produced by the algorithm provided a measure of thereliability of both the data generated as well as the algorithm'sanalysis of the data. The high level of self-consistency for theANTIGEN14 data set (over 99%) suggests that one can trust the results ofthe algorithm for this data set with a high level of confidence.

Example 3 Analysis of Multiple Data Sets: ANTIGEN39

When there are input data sets for more than one experiment, normalizedintensity matrices are first generated as described above for eachindividual experiment. Normalized values above a threshold value(typically set to 4) are set to the corresponding threshold value. Thisprevents any single normalized intensity value from having too muchinfluence on the average value for that antibody pair. A singlenormalized matrix is generated from the individual normalized matricesby taking the average of the normalized intensity values over allexperiments for each antibody pair for which there is data. Antibodypairs with no corresponding intensity values are flagged. The generationof the dissimilarity matrix is as described above with the exceptionthat the fraction of the positions at which two rows, i and j differonly considers the number of positions for which both rows have anintensity value. If the two rows have no such positions, then thedissimilarity value is set arbitrarily high and flagged.

Five experiments were conducted using ANTIGEN39 antibodies, usingmethods described in Examples 1 and 2, and throughout the description.The clustering results for the five input data sets of ANTIGEN39antibodies are summarized in FIG. 6A, FIG. 18, FIG. 19, and Table 30.The results show that there were a large number of clusters of varyingdegree of similarity. This suggests there were several differentepitopes, some of which may overlap. For example, the cluster containingantibodies 1.17, 1.55, 1.16, 1.11, and 1.12 and the cluster containing1.21, 2.12, 2.38, 2.35, and 2.1 are fairly closely related (eachantibody pair with the exception of 2.35 and 1.11 being no more than 25%different). This high degree of similarity across the two clusterssuggests that the two different epitopes may have a high degree ofsimilarity

In order to test the algorithm's ability to produce consistentclustering results, the five data sets were also independentlyclustered. The clustering results for the different experiments aresummarized in FIGS. 6B-6F and in FIGS. 20-30. FIG. 30 summarizes theclusters for each of the individual data sets and for the combined dataset with all of the antibodies for the five experiments. FIG. 6B showsthe dendrogram for the ANTIGEN39 antibodies for Experiment 1: Antibodies1.12, 1.63, 1.17, 1.55, and 2.12 consistently clustered together in thisexperiment as well as in other experiments as do antibodies 1.46, 1.31,2.17, and 1.29. FIG. 6C shows the dendrogram for the ANTIGEN39antibodies for Experiment 2: Antibodies 1.57 and 1.61 consistentlyclustered together in this experiment as well as in other experiments.

FIG. 6D shows the dendrogram for the ANTIGEN39 antibodies for Experiment3: Antibodies 1.55, 1.12, 1.17, 2.12, 1.11, and 1.21 consistentlyclustered together in this experiment as well as in other experiments.FIG. 6E shows the dendrogram for the ANTIGEN39 antibodies for experiment4: Antibodies 1.17, 1.16, 1.55, 1.11, and 1.12 consistently clusteredtogether in this experiment as well as in other experiments as doantibodies 1.31, 1.46, 1.65, and 1.29, as well as antibodies 1.57 and1.61. FIG. 6F shows the dendrogram for the ANTIGEN39 antibodies forexperiment 5: Antibodies 1.21, 1.12, 2.12, 2.38, 2.35, and 2.1consistently clustered together in this experiment as well as in otherexperiments.

In general, the clustering algorithm produced consistent results bothamong the individual experiments and between the combined and individualdata sets. Antibodies which cluster together or are in neighboringclusters for multiple individual data sets also cluster together or bein neighboring clusters for the combined data set. For example, thecells with back hatching indicate antibodies that consistently clusteredtogether in the combined data set and in all of the data sets in whichthey were present (Experiments 1, 3, 4, and 5). Similarly, the cellswith forward hatching indicate the antibodies that consistentlyclustered together in the combined data set and in Experiments 1, 4, and5. These results indicate that the algorithm produces consistentclustering results both across multiple individual experiments and thatit retains the consistency upon the merging of multiple data sets.

Finally, there is a high level of self-consistency for the data withregard to revealing whether or not two antibodies compete for the sameepitope. The percent of antibody pairs for which the data consistentlyreveals whether or not they compete for the same epitope is summarizedfor each data set in Table 2, above. Table 2 reveals that theconsistency was nearly 90% for four out of the five individual data setsas well as for the combined data set.

Example 4 Analysis of a Small Set of IL-8 Human Monoclonal AntibodiesUsing the Competitive Pattern Recognition Data Analysis Process

A small set of well-characterized human monoclonal antibodies developedagainst IL-8, a proinflammatory mediator, was used to evaluate theprogram applying the CPR process. Previously, plate-based ELISAs hadshown that antibodies within the set bound two different epitopes: HR26,a215, and D111 recognized one epitope, whereas K221 and a33 competed fora second epitope. Further analysis using epitope mapping studies showedthat HR26, a809, and a928 bound to the same or overlapping epitopes,while a837 bound to a different epitope.

In a new experiment to determine whether the CPR process was capable ofcorrectly clustering antibodies, the process was tested on a set ofseven IL-8 antibodies, including some of the monoclonal antibodieslisted above. The results are summarized in the dendrograms shown inFIG. 7A. The dendrogram on the left was generated by clustering columns,and the dendrogram on the right was generated by clustering rows of thebackground-normalized signal intensity matrix. Both dendrogramsindicated that there were two epitopes for a dissimilarity cut-off of0.25: one epitope recognized by HR26, a215, a203, a393, and a452, and asecond epitope recognized by K221 and a33.

These results using the CPR process to cluster antibodies wereconsistent with the data from plate-based ELISA assays summarized above.The results obtained using the CPR process indicated that the targetantigen appeared to have two distinct epitopes, confirming the resultsseen using plate-based ELISA assays. Using the CPR process forclustering indicated that HR26 and a215 clustered together, as did K221and a33, again consistent with the results from plate-based ELISAassays.

The degree of similarity between the two dendrograms provided a measureof the self-consistency of the analyses performed by this process.Ideally, the two dendrograms (the one on the left generated byclustering columns and the one on the right generated by clusteringrows) should have been identical for the following reason: if Antibody#1 and Antibody #2 compete for the same epitope, then the intensityshould be low when Antibody #1 is the reference antibody and Antibody #2is the probe antibody, as well as when Antibody #2 is the referenceantibody and Antibody #1 is the probe antibody. Likewise, when the twoantibodies bind to different epitopes, the intensities should beuniformly high. By this reasoning, the degree of similarity between tworows of the signal intensity matrix should be the same as between twocolumns of the similarity matrix. In the present example, thedendrograms on the left- and right-hand side of FIG. 7A are nearlyidentical. In each case, the same antibodies appeared in the twoclusters. This high level of self-consistency between row and columnclusterings suggested that the experimental protocol, together with theprocess, produces robust results.

Example 5 Analysis of Multiple Data Sets of IL-8 Antibodies Using theCompetitive Pattern Recognition (CPR) Data Analysis Process

Multiple screening experiments using IL-8 antibodies were carried out,generating multiple data sets. Normalized intensity matrices were firstgenerated as described above for the matrices for each individualexperiment. Normalized values greater than a user-defined thresholdvalue were set to the user-defined threshold value. High-intensityvalues were assigned to the threshold value to prevent any singleintensity value from having too much weight when the average normalizedintensity value was computed for that particular pair of antibodies in asubsequent step. The rows and columns of the average normalizedintensity matrix corresponded to the set of “unique” antibodiesidentified using the methods of the present invention. These “unique”antibodies were identified from among all the antibodies used in all theexperiments. The average intensity was computed for each cell in thismatrix for which there was at least one intensity value. Cellscorresponding to antibody pairs with no data were identified as missingdata points. Generation of the dissimilarity matrix was as describedabove, except that the fraction was determined based on the number ofpositions at which two rows differed relative to the total number ofpositions for which both rows had intensity values. If the two rows hadno common data, then the dissimilarity value for the corresponding cellwas flagged and set arbitrarily high, so the corresponding antibodieswould not be grouped together as an artifact.

The clustering results for a set of monoclonal antibodies from fiveoverlapping sets of monoclonal antibodies are summarized in FIG. 7B andTable 3 (below). These dendrograms corroborate the results showing thereare two different epitopes on the target antigen. The first epitope isdefined by monoclonal antibodies a809, a928, HR26, a215, and D111 andthe second epitope is defined by monoclonal antibodies a837, K221, a33,a142, and a358, a203, a393, and a452. The lengths of the branchesconnecting the clusters indicated that, whereas the first cluster wasvery different from the other two, the second and third clusters weresimilar to each other.

To test the capacity of the CPR process to produce consistent resultsacross separate experiments, the five data sets were also independentlyclustered. The clustering results for the different experiments aresummarized in the dendrograms shown in FIGS. 7A, 7B, 7C, and Table B.These dendrograms demonstrated that the CPR clustering process producedconsistent results among the individual experiments and between combinedand individual data sets. Each dendrogram had two major branches,indicating two epitopes. Antibodies that clustered together for multipleindividual data sets also clustered together or were in neighboringclusters for the combined data set. As shown in Table 3, below, therewere only two minor discrepancies in the clustering results acrossdifferent experiments or between an individual experiment and thecombined data set, where these discrepancies are indicated by bold typein Table 3. In a data set generated in Experiment 3, D111 clustered withantibodies a33 and K221, instead of HR26 and a215. In a data setgenerated in Experiment 4, antibodies a203, a393, and a452 appeared inthe first cluster, whereas in another experiment (as well as in thecombined data set), they appeared in a second cluster. This slightdifference is likely attributable to differences in individual antibodyaffinity between experiments in which the antibody is used as a probeantibody and experiments in which the same antibody is used as areference antibody. Antibodies with lower affinity may have a reducedcapacity to capture antigen out of the solution when used as a referenceantibody. However, the overall similarity of the clustering results, aswell as the grouping of the antigens, indicated that the processproduced consistent clustering results that were in good agreement withresults from other experiments across multiple individual experiments,and that the results remained consistent when multiple data sets weremerged.

Finally, there was a high level of consistency in clustering results foreach of these data sets when the process was used to cluster by rows andby columns, for the individual and combined data sets. The onlydiscrepancy in the clustering results between row and column clusteringswas with D111 in the third data set, in which it clustered withantibodies HR26 and a215 when row clustering was performed, whereas D111clustered with antibodies a33 and K221 when column clustering wasperformed.

TABLE B Results of Clustering for Individual and Combined Data SetsExpt1 Expt1 Expt2 Expt2 Expt3 Expt3 Expt4 Expt4 Expt5 Expt5 Comb CombCluster Rows Cols Rows Cols Rows Cols Rows Cols Rows Cols Rows Cols 1a809 a809 D111 D111 D111 HR26 HR26 HR26 HR26 HR26 a809 a809 a928 a928HR26 HR26 HR26 a215 a215 a215 a215 a215 a928 a928 HR26 HR26 a215 a215a215 a203 a203 D111 D111 a393 a393 HR26 HR26 a452 a452 a215 a215 2 a837a837 a33 a33 a33 D111 a33 a33 a33 a33 a837 a837 K221 K221 K221 K221 K221a33 K221 K221 K221 K221 a33 a33 K221 a203 a203 K221 K221 a393 a393 a142a142 a452 a452 a358 a358 a142 a142 a203 a203 a358 a358 a393 a393 a452a452

It will be understood by those of skill in the art that numerous andvarious modifications can be made without departing from the spirit ofthe present invention. Therefore, it should be clearly understood thatthe forms of the present invention are illustrative only and are notintended to limit the scope of the present invention.

1. An antibody competition assay method for determining antibodies that bind to an epitope on an antigen, comprising: providing a set of antibodies that bind to an antigen; labeling each antibody of said set to form a labeled reference antibody set such that each labeled reference antibody is distinguishable from every other labeled reference antibody in said labeled reference antibody set; selecting a probe antibody from said set of antibodies that bind to the antigen; contacting said probe antibody with said labeled reference antibody set in the presence of said antigen; detecting said probe antibody in a complex comprising said antigen, one labeled reference antibody bound to said antigen, and said labeled probe antibody bound to said antigen; identifying each said labeled reference antibody bound to said antigen in each said complex; determining whether said probe antibody competes with any reference antibody in said labeled reference antibody set, wherein competition indicates that said probe antibody binds to the same epitope as another antibody in said set of antibodies that bind to an antigen.
 2. The method of claim 1, further comprising labeling said probe antibody to form a labeled probe antibody.
 3. The method of claim 2, wherein said labeled probe antibody comprises a label selected from an enzymatic label, a colorimetric label, a fluorescent label, or a radioactive label.
 4. The method of claim 1, further comprising providing a labeled detection antibody for detecting said probe antibody.
 5. The method of claim 4, wherein said labeled detection antibody comprises a label selected from an enzymatic label, a colorimetric label, a fluorescent label, or a radioactive label.
 6. The method of claim 1, wherein each antibody of set of antibodies that bind to an antigen is labeled with a uniquely colored bead to form a labeled reference antibody set such that each labeled reference antibody is distinguishable from every other labeled reference antibody in said labeled reference antibody set.
 7. The method of claim 6, wherein said each uniquely colored bead has a distinct emission spectrum.
 8. The method of claim 6, wherein said probe antibody is a labeled probe antibody.
 9. The method of claim 6, further comprising providing a labeled detection antibody for detecting said probe antibody.
 10. The method of claim 1, further comprising a method for characterizing antibodies based on binding characteristics, comprising: providing input data representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen; normalizing said input data to generate a normalized intensity matrix; computing at least one dissimilarity matrix comprising generating a threshold matrix from said normalized intensity matrix and computing a dissimilarity matrix from said threshold matrix; and clustering antibodies based on dissimilarity values in cells of said dissimilarity matrix, to determine epitope binding patterns of said set of antibodies that bind to an antigen.
 11. The method of claim 10, wherein the Competitive Pattern Recognition (CPR) process is used for characterizing antibodies based on binding characteristics.
 12. The method of claim 10 wherein said input data is generated by a high throughput competitive antibody assay.
 13. The method of claim 12, wherein said high throughput competitive antibody assay is the Multiplexed Competitive Antibody Binning (MCAB) assay.
 14. The method of claim 10, wherein said input data comprises signal intensity values representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen.
 15. The method of claim 14, wherein said input data comprising signal intensity values representing the outcomes of at least one antibody competition assay comprises input data stored in matrix form.
 16. The method of claim 15, wherein said input data stored in matrix form comprises a two-dimensional matrix.
 17. The method of claim 15, wherein said data stored in matrix form comprises a multidimensional matrix.
 18. The method of claim 15 wherein said input data stored in matrix form comprises a plurality of matrices.
 19. The method of claim 15, wherein said input data stored in matrix form comprises signal intensity values representing the outcomes of an antibody competition assay carried out using a multi-well format, wherein each cell of said matrix represents the outcome of the assay carried out in one well of said multi-well format.
 20. The method of claim 15, wherein said normalizing said input data to generate a normalized intensity matrix comprises generating a background-normalized intensity matrix by subtracting a first matrix comprising signal intensity values from a first antibody competition assay in which antigen was not added, from a second matrix comprising signal intensity values from a second antibody competition assay in which antigen was added.
 21. The method of claim 20, comprising setting a minimum threshold value for blocking buffer values and adjusting any blocking buffer values below said threshold value to said threshold value prior to generating said normalized intensity matrix.
 22. The method of claim 21, further comprising dividing each value in a column of said background-normalized intensity matrix by the blocking buffer intensity value for said column.
 23. The method of claim 22, wherein said normalizing step further comprises normalizing relative to the baseline signal for probe antibodies, comprising dividing each said column of said intensity-normalized matrix by its corresponding diagonal value to generate a final intensity-normalized matrix.
 24. The method of claim 23, wherein each said diagonal value is compared with a user-defined threshold value and any said diagonal value below said user-defined threshold value is adjusted to said threshold value prior to said dividing each column by its corresponding diagonal value.
 25. The method of claim 21, wherein said normalizing step further comprises generating an intensity-normalized matrix by dividing each value in a row of said background-normalized intensity matrix by the blocking buffer intensity value for said row.
 26. The method of claim 25, wherein said normalizing step further comprises normalizing relative to the baseline signal for probe antibodies, comprising dividing each said row of said intensity-normalized matrix by its corresponding diagonal value to generate a final intensity-normalized matrix.
 27. The method of claim 26, wherein each said diagonal value is compared with a user-defined threshold value and any said diagonal value below said user-defined threshold value is adjusted to said threshold value prior to said dividing each row by its corresponding diagonal value.
 28. The method of claim 20, wherein said generating said threshold matrix comprises setting the normalized valued in each cell of said normalized intensity matrix to a value of one (1) or zero (0), wherein normalized values less than or equal to a threshold value are set to a value of zero (0) and normalized values greater to said threshold value are set to a value of one (1).
 29. The method of claim 28, wherein said at least one dissimilarity matrix is computed by providing said threshold matrix of ones and zeroes and determining the number of positions in which each pair of rows differs.
 30. The method of claim 29, wherein a plurality of dissimilarity matrices are computing using a plurality of threshold values.
 31. The method of claim 30, wherein the average of said plurality of dissimilarity matrices is computed.
 32. The method of claim 31, comprising providing said average of said plurality of dissimilarity matrices as input to said clustering step.
 33. The method of claim 10, wherein said clustering antibodies based on said dissimilarity values in cells of said dissimilarity matrix comprises hierarchical clustering.
 34. The method of claim 33, wherein said hierarchical clustering comprises generating a hierarchy of nested subsets of antibodies within said set of antibodies that bind to an antigen, comprising: determining the pair of antibodies in said set having the lowest dissimilarity value; determining the pair of antibodies having the next lowest dissimilarity value; interatively repeating said determining said pair of antibodies having the next lowest dissimilarity value until one pair of antibodies remains, such that a hierarchy of nested subsets is generated that indicates the similarity of competition patterns within said set of antibodies; and determining clusters based on competition patterns.
 35. The method of claim 34, wherein data from said clustering step is captured.
 36. The method of claim 35, wherein data is captured by automated means.
 37. The method of claim 34, wherein said clustering step generates a display.
 38. The method of claim 37, wherein said display is in a format compatible with data input device or computer.
 39. The method of claim 37, wherein said display comprises a dissimilarity matrix.
 40. The method of claim 39, wherein clusters are determined by visual inspection of the dissimilarity value in each cell of said dissimilarity matrix.
 41. The method of claim 39, wherein said dissimilarity matrix comprises cells having a visual indicator of the cluster to which the antibody pair represented by said cell belongs.
 42. The method of claim 41, wherein said visual indicator is a color.
 43. The method of claim 41, wherein said visual indicator is shading.
 44. The method of claim 41, wherein said visual indicator is patterning.
 45. The method of claim 37, wherein said display is a dendrogram defined by dissimilarity values computed for said set of antibodies.
 46. The method of claim 45, wherein said dendrogram comprises branches representing antibodies in said set of antibodies, wherein the arrangement of branches represents relationships between antibodies within said set of antibodies, further wherein said arrangement represents clusters of antibodies within said set of antibodies.
 47. The method of claim 46, further wherein said dendrogram comprises branches wherein the length of any said branch represents the degree of similarity between the binding pattern of antibodies or cluster of antibodies represented by said branch.
 48. The method of claim 10, comprising providing input data representing the outcomes of a plurality of antibody competition assays, wherein each assay represents an individual experiment using a set of antibodies that bind to an antigen, further wherein each said experiment comprises at least one antibody that is also tested in at least one other experiment.
 49. The method of claim 48, comprising generating an individual normalized intensity matrix for each said individual experiment and further comprising generating a single normalized intensity matrix by computing the average intensity value of each antibody pair represented in each individual normalized intensity matrix.
 50. The method of claim 49, further comprising generating a single dissimilarity matrix representing each antibody pair tested in said plurality of antibody competition assays.
 51. A method for characterizing antibodies based on binding characteristics, comprising: providing a labeled reference antibody set such that each labeled reference antibody in said set is distinguishable from every other labeled reference antibody in said labeled reference antibody set, and wherein each antibody binds to an antigen; selecting a probe antibody from said set of antibodies that binds to the antigen; contacting said probe antibody with said labeled reference antibody set in the presence of said antigen; detecting said probe antibody in a complex comprising said antigen, one labeled reference antibody bound to said antigen, and said labeled probe antibody bound to said antigen; identifying each said labeled reference antibody bound to said antigen in each said complex; determining whether said probe antibody competes with any reference antibody in said labeled reference antibody set, wherein competition indicates that said probe antibody binds to the same epitope as another antibody in said set of antibodies that bind to an antigen; providing input data representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen, further wherein said input data comprises signal intensity values representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen; storing said input data in matrix format; subtracting a first matrix comprising signal intensity values from a first antibody competition assay in which antigen was not added, from a second matrix comprising signal intensity values from a second antibody competition assay in which antigen was added, to form a background normalized intensity matrix; calculating the value in each diagonal cell of said background normalized intensity matrix; determining cells in each column of said background normalized intensity matrix having values significantly higher than the value in each diagonal cell of said column; and grouping columns having similar binding patterns as a bin.
 52. The method of claim 51, wherein said cells in said background normalized intensity matrix having values significantly higher than said value in each corresponding diagonal cell of said column have values at least two times said value in each diagonal cell. 